paper_name stringlengths 4 421 | paper_url stringlengths 21 200 | paper_authors listlengths 0 125 | paper_abstract stringlengths 0 43.4k | paper_code stringlengths 1 149 | conf stringlengths 6 18 |
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Simplifying Medical Ultrasound - 6th International Workshop, ASMUS 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 28, 2025, Proceedings | https://doi.org/10.1007/978-3-032-06329-8 | [
"Dong Ni",
"J. Alison Noble",
"Ruobing Huang",
"Wufeng Xue"
] | # | MICCAI2025 | |
DualTrack: Sensorless 3D Ultrasound needs Local and Global Context | https://doi.org/10.1007/978-3-032-06329-8_1 | [
"Paul F. R. Wilson",
"Matteo Ronchetti",
"Rüdiger Göbl",
"Viktoria Markova",
"Sebastian Rosenzweig",
"Raphael Prevost",
"Parvin Mousavi",
"Oliver Zettinig"
] | Three-dimensional ultrasound (US) offers many clinical advantages over conventional 2D imaging, yet its widespread adoption is limited by the cost and complexity of traditional 3D systems. Sensorless 3D US, which uses deep learning to estimate a 3D probe trajectory from a sequence of 2D US images, is a promising altern... | # | MICCAI2025 |
Modulated INR with Prior Embeddings for Ultrasound Imaging Reconstruction | https://doi.org/10.1007/978-3-032-06329-8_2 | [
"Rémi Delaunay",
"Christoph Hennersperger",
"Stefan Wörz"
] | Ultrafast ultrasound imaging enables visualization of rapid physiological dynamics by acquiring data at exceptionally high frame rates. However, this speed often comes at the cost of spatial resolution and image quality due to unfocused wave transmissions and associated artifacts. In this work, we propose a novel modul... | # | MICCAI2025 |
DiffUS: Differentiable Ultrasound Rendering from Volumetric Imaging | https://doi.org/10.1007/978-3-032-06329-8_3 | [
"Noe Bertramo",
"Gabriel Duguey",
"Vivek Gopalakrishnan"
] | Intraoperative ultrasound imaging provides real-time guidance during numerous surgical procedures, but its interpretation is complicated by noise, artifacts, and poor alignment with high-resolution preoperative MRI/CT scans. To bridge the gap between reoperative planning and intraoperative guidance, we present DiffUS, ... | # | MICCAI2025 |
3D Heart Reconstruction from Sparse Pose-Agnostic 2D Echocardiographic Slices | https://doi.org/10.1007/978-3-032-06329-8_4 | [
"Zhurong Chen",
"Jinhua Chen",
"Wei Zhuo",
"Wufeng Xue",
"Dong Ni"
] | Echocardiography (echo) plays an indispensable role in the clinical practice of heart diseases. However, ultrasound imaging typically provides only two-dimensional (2D) cross-sectional images from a few specific views, making it challenging to interpret and inaccurate for estimation of clinical parameters like the volu... | # | MICCAI2025 |
3D Ultrasound Volume Reconstruction Using a CNN-Transformer Model and IMU Data | https://doi.org/10.1007/978-3-032-06329-8_5 | [
"Mark Wijkhuizen",
"Chrissy A. Adriaans",
"Lennard M. van Karnenbeek",
"Tiziano Natali",
"Theo Ruers",
"Freija Geldof",
"Behdad Dashtbozorg"
] | # | MICCAI2025 | |
Optimization-Based Calibration for Intravascular Ultrasound Volume Reconstruction | https://doi.org/10.1007/978-3-032-06329-8_6 | [
"Karl-Philippe Beaudet",
"Sidaty El Hadramy",
"Philippe C. Cattin",
"Juan Verde",
"Stéphane Cotin"
] | Intraoperative ultrasound images are inherently challenging to interpret in liver surgery due to the limited field of view and complex anatomical structures. Bridging the gap between preoperative and intraoperative data is crucial for effective surgical guidance. 3D IntraVascular UltraSound (IVUS) offers a potential so... | # | MICCAI2025 |
Robust Rigid MRI-TRUS Registration in Prostate Cancer Using Attention-CNN and ICP | https://doi.org/10.1007/978-3-032-06329-8_7 | [
"Manasi Kattel",
"Benjamin Billot",
"Federica Facente",
"Hervé Delingette",
"Nicholas Ayache"
] | # | MICCAI2025 | |
Det-SAMReg: Few-Shot Medical Image Registration Using Vision Foundation Models | https://doi.org/10.1007/978-3-032-06329-8_8 | [
"Mengting Yang",
"Qilin Wang",
"Shiqi Huang",
"Wen Yan",
"Yipeng Hu",
"Zhe Min"
] | # | MICCAI2025 | |
DARK: Dynamic Graphs Based Angle-Aware Registration of Knee Ultrasound Point Clouds | https://doi.org/10.1007/978-3-032-06329-8_9 | [
"Injune Hwang",
"Stephen J. Mellon",
"Shihfan Jack Tu"
] | # | MICCAI2025 | |
The Impact of Biomechanical Quantities on PINNs-Based Medical Image Registration | https://doi.org/10.1007/978-3-032-06329-8_10 | [
"Shixing Ma",
"Zhaoxi Lin",
"Xinzhe Du",
"Yipeng Hu",
"Zhe Min"
] | # | MICCAI2025 | |
VidFuncta: Towards Generalizable Neural Representations for Ultrasound Videos | https://doi.org/10.1007/978-3-032-06329-8_11 | [
"Julia Wolleb",
"Florentin Bieder",
"Paul Friedrich",
"Hemant D. Tagare",
"Xenophon Papademetris"
] | Ultrasound is widely used in clinical care, yet standard deep learning methods often struggle with full video analysis due to non-standardized acquisition and operator bias. We offer a new perspective on ultrasound video analysis through implicit neural representations (INRs). We build on Functa, an INR framework in wh... | https://github.com/JuliaWolleb/VidFuncta_public | MICCAI2025 |
From Transthoracic to Transesophageal: Cross-Modality Generation Using LoRA Diffusion | https://doi.org/10.1007/978-3-032-06329-8_12 | [
"Emmanuel Oladokun",
"Yuxuan Ou",
"Anna Novikova",
"Daria P. Kulikova",
"Sarina Thomas",
"Jurica Sprem",
"Vicente Grau"
] | Deep diffusion models excel at realistic image synthesis but demand large training sets-an obstacle in data-scarce domains like transesophageal echocardiography (TEE). While synthetic augmentation has boosted performance in transthoracic echo (TTE), TEE remains critically underrepresented, limiting the reach of deep le... | # | MICCAI2025 |
DiFUSAL: Diffusion-Based Fetal Ultrasound Synthesis with Active Learning | https://doi.org/10.1007/978-3-032-06329-8_13 | [
"Maryam Arjemandi",
"Salma Hassan",
"Hu Wang",
"Saudabi Valappi",
"Mohammad Yaqub"
] | # | MICCAI2025 | |
Motion-Enhanced Cardiac Anatomy Segmentation via an Insertable Temporal Attention Module | https://doi.org/10.1007/978-3-032-06329-8_14 | [
"Md. Kamrul Hasan",
"Guang Yang",
"Choon Hwai Yap"
] | Cardiac anatomy segmentation is useful for clinical assessment of cardiac morphology to inform diagnosis and intervention. Deep learning (DL), especially with motion information, has improved segmentation accuracy. However, existing techniques for motion enhancement are not yet optimal, and they have high computational... | # | MICCAI2025 |
UGFNet: Uncertainty-Guided Graph Neural Network with Frequency-Aware Feature Fusion for Breast Ultrasound Segmentation | https://doi.org/10.1007/978-3-032-06329-8_15 | [
"Hyunmin Kong",
"Jitae Shin"
] | # | MICCAI2025 | |
L-FUSION: Laplacian Fetal Ultrasound Segmentation and Uncertainty Estimation | https://doi.org/10.1007/978-3-032-06329-8_16 | [
"Johanna P. Müller",
"Robert Wright",
"Thomas G. Day",
"Lorenzo Venturini",
"Samuel Budd",
"Hadrien Reynaud",
"Joseph V. Hajnal",
"Reza Razavi",
"Bernhard Kainz"
] | # | MICCAI2025 | |
Diffusion-Based Iterative Counterfactual Explanations for Fetal Ultrasound Image Quality Assessment | https://doi.org/10.1007/978-3-032-06329-8_17 | [
"Paraskevas Pegios",
"Manxi Lin",
"Nina Weng",
"Morten Bo Søndergaard Svendsen",
"Zahra Bashir",
"Siavash Arjomand Bigdeli",
"Anders Nymark Christensen",
"Martin Grønnebæk Tolsgaard",
"Aasa Feragen"
] | # | MICCAI2025 | |
Guide2Heart: Proximity Guidance for Standard Echocardiographic View | https://doi.org/10.1007/978-3-032-06329-8_18 | [
"Gajendra Singh",
"Aiman Farooq",
"Azad Singh",
"Deepak Mishra",
"Rahul Choudhary",
"Pushpinder Singh Khera"
] | # | MICCAI2025 | |
HiProtoNet: Hyperbolic Hierarchy-Aware Part Prototypes for Aortic Stenosis Severity Classification | https://doi.org/10.1007/978-3-032-06329-8_19 | [
"Hooman Vaseli",
"Victoria Wu",
"Diane Kim",
"Michael Y. Tsang",
"Ang Nan Gu",
"Christina Luong",
"Purang Abolmaesumi",
"Teresa S. M. Tsang"
] | # | MICCAI2025 | |
COVID-19 Severity Prediction from Lung Ultrasound via Dynamic Gated Multi-instance Learning | https://doi.org/10.1007/978-3-032-06329-8_20 | [
"Chen Lin",
"Guang-Quan Zhou",
"Wufeng Xue",
"Dong Ni"
] | # | MICCAI2025 | |
WiseLVAM: A Novel Framework For Left Ventricle Automatic Measurements | https://doi.org/10.1007/978-3-032-06329-8_21 | [
"Durgesh Kumar Singh",
"Qing Cao",
"Sarina Thomas",
"Ahcène Boubekki",
"Robert Jenssen",
"Michael Kampffmeyer"
] | Clinical guidelines recommend performing left ventricular (LV) linear measurements in B-mode echocardiographic images at the basal level -- typically at the mitral valve leaflet tips -- and aligned perpendicular to the LV long axis along a virtual scanline (SL). However, most automated methods estimate landmarks direct... | https://github.com/SFI-Visual-Intelligence/wiselvam.git | MICCAI2025 |
Learning to Stop: Reinforcement Learning for Efficient Patient-Level Echocardiographic Classification | https://doi.org/10.1007/978-3-032-06329-8_22 | [
"Woo-Jin Cho Kim",
"Jorge Oliveira",
"Arian Beqiri",
"Alexander Thorley",
"Jordan Strom",
"Jamie O'Driscoll",
"Rajan Sharma",
"Jeremy Slivnick",
"Roberto Lang",
"Alberto Gómez",
"Agisilaos Chartsias"
] | # | MICCAI2025 | |
TREAT-Net: Tabular-Referenced Echocardiography Analysis for Acute Coronary Syndrome Treatment Prediction | https://doi.org/10.1007/978-3-032-06329-8_23 | [
"Diane Kim",
"Minh Nguyen Nhat To",
"Sherif Abdalla",
"Teresa S. M. Tsang",
"Purang Abolmaesumi",
"Christina Luong"
] | Coronary angiography remains the gold standard for diagnosing Acute Coronary Syndrome (ACS). However, its resource-intensive and invasive nature can expose patients to procedural risks and diagnostic delays, leading to postponed treatment initiation. In this work, we introduce TREAT-Net, a multimodal deep learning fram... | # | MICCAI2025 |
Anatomically Constrained Transformers for Cardiac Amyloidosis Classification | https://doi.org/10.1007/978-3-032-06329-8_24 | [
"Alexander Thorley",
"Agis Chartsias",
"Jordan Strom",
"Roberto Lang",
"Jeremy Slivnick",
"Jamie O'Driscoll",
"Rajan Sharma",
"Dipak Kotecha",
"Jinming Duan",
"Alberto Gómez"
] | Cardiac amyloidosis (CA) is a rare cardiomyopathy, with typical abnormalities in clinical measurements from echocardiograms such as reduced global longitudinal strain of the myocardium. An alternative approach for detecting CA is via neural networks, using video classification models such as convolutional neural networ... | # | MICCAI2025 |
Segmentation, Classification, and Synthesis for Brain Tumors and Traumatic Brain Injuries - MICCAI 2025 Challenges: BraTS-Lighthouse 2025 and AIMS-TBI 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025, Proceedings, Part I | https://doi.org/10.1007/978-3-032-16365-3 | [
"Spyridon Bakas",
"Emily L. Dennis",
"Mehdi Astaraki",
"Ujjwal Baid",
"Gian Marco Conte",
"Martha Foltyn-Dumitru",
"Zhifan Jiang",
"Dominic LaBella",
"Marie-Christin Metz",
"Udunna Anazodo",
"Maria Correia de Verdier",
"Florian Kofler",
"Hongwei Bran Li",
"Marius George Linguraru",
"Naza... | # | MICCAI2025 | |
My Model Is Better Than Yours! Statistically-Aware Ranking for Fair Benchmarking of AI Models | https://doi.org/10.1007/978-3-032-16365-3_1 | [
"Spyridon Bakas",
"Siddhesh P. Thakur",
"Ujjwal Baid",
"Akis Linardos",
"Sarthak Pati",
"Jimit Doshi",
"Russell T. Shinohara"
] | # | MICCAI2025 | |
EGASegNet: An Extreme Group-Aware Segmentation Network for Glioma Segmentation | https://doi.org/10.1007/978-3-032-16365-3_2 | [
"Liwei Jin",
"Yanjun Peng"
] | # | MICCAI2025 | |
Pre- and Post-Treatment Glioma Segmentation with the Medical Imaging Segmentation Toolkit | https://doi.org/10.1007/978-3-032-16365-3_3 | [
"Adrian Celaya",
"Tucker J. Netherton",
"Dawid Schellingerhout",
"Caroline Chung",
"Beatrice Riviere",
"David Fuentes"
] | Medical image segmentation continues to advance rapidly, yet rigorous comparison between methods remains challenging due to a lack of standardized and customizable tooling. In this work, we present the current state of the Medical Imaging Segmentation Toolkit (MIST), with a particular focus on its flexible and modular ... | # | MICCAI2025 |
µPUA-Net: PowerMLP Model Size Shrinking Method with Accuracy Maintaining | https://doi.org/10.1007/978-3-032-16365-3_4 | [
"Yu-Shan Chou",
"You-Jin Liu",
"Kai-Lun Pien",
"Tong-Hou Cheong",
"Chieh-Chen Yu",
"Ying-Hui Cheng",
"Yu-Hsuan Chiang",
"E. Ray Hsieh",
"Chien-Chang Chen"
] | # | MICCAI2025 | |
On-the-Fly Data Augmentation for Brain Tumor Segmentation | https://doi.org/10.1007/978-3-032-16365-3_5 | [
"Ishika Jain",
"Siri Willems",
"Steven Latré",
"Tom De Schepper"
] | # | MICCAI2025 | |
Segmentation of Pre- and Post- Operative Glioma Tumors Using Swin UNETR and BraTS-25 Challenge Data | https://doi.org/10.1007/978-3-032-16365-3_6 | [
"Mohammad Tufail Sheikh",
"Satyajit Maurya",
"Anup Singh"
] | # | MICCAI2025 | |
Enhancing Tumor Subregion Segmentation Using Domain Adaptation, Pseudo-Labeling, and Post-Processing Optimization | https://doi.org/10.1007/978-3-032-16365-3_7 | [
"Ajesh Saviour Paravila"
] | # | MICCAI2025 | |
PTransBTS: A Hybrid Transformer Integrating Priors for Brain Tumor Segmentation | https://doi.org/10.1007/978-3-032-16365-3_8 | [
"Haitao Yu",
"Yanjun Peng"
] | # | MICCAI2025 | |
Efficient Meningioma Tumor Segmentation Using Ensemble Learning | https://doi.org/10.1007/978-3-032-16365-3_9 | [
"Mohammad Mahdi Danesh Pajouh",
"Sara Saeedi"
] | Meningiomas represent the most prevalent form of primary brain tumors, comprising nearly one-third of all diagnosed cases. Accurate delineation of these tumors from MRI scans is crucial for guiding treatment strategies, yet remains a challenging and time-consuming task in clinical practice. Recent developments in deep ... | # | MICCAI2025 |
Brain Tissue Context for Enhancing Brain Tumor Segmentation: A Contribution to BraTS 2025 | https://doi.org/10.1007/978-3-032-16365-3_10 | [
"Mehdi Astaraki",
"Farangis Sajadi Moghadam",
"Iuliana Toma-Dasu"
] | # | MICCAI2025 | |
DeSURVAE: A Dual-Encoder Dual-Decoder Neural Network for GTV Semantic Segmentation of Meningioma Brain Tumor in Radiotherapy Planning | https://doi.org/10.1007/978-3-032-16365-3_12 | [
"Nima Sadeghzadeh",
"Jason A. Correia",
"Samantha J. Holdsworth",
"Poul M. F. Nielsen",
"Michael Dragunow",
"Richard L. M. Faull",
"Hamid Abbasi"
] | # | MICCAI2025 | |
Boundary-Aware Approach for Meningioma Segmentation in Radiotherapy Planning MRI | https://doi.org/10.1007/978-3-032-16365-3_13 | [
"Valeriia Abramova",
"Agustin Cartaya Lathulerie",
"Uma M. Lal-Trehan Estrada",
"Cansu Yalcin",
"Rachika E. Hamadache",
"Clara Lisazo",
"Micaela Rivas Díaz",
"Adrià Casamitjana",
"Arnau Oliver",
"Xavier Lladó"
] | # | MICCAI2025 | |
Condition-Based Ensemble Modelling of Swin UNETR and 3D U-Net for Meningioma Segmentation in Radiotherapy Planning | https://doi.org/10.1007/978-3-032-16365-3_14 | [
"Sanskriti Srivastava",
"Kuldeep Raghuwanshi",
"Anup Singh"
] | # | MICCAI2025 | |
Segmentation of Pre and Post-treatment Brain Metastases Using nnU-Nets | https://doi.org/10.1007/978-3-032-16365-3_15 | [
"Maria Bancerek",
"Piotr Rudzki",
"Jakub Nalepa"
] | # | MICCAI2025 | |
Automated Segmentation for the Brain Tumor Segmentation (BraTS) Metastases 2025 Challenge Using Multi-Architectural Deep Learning | https://doi.org/10.1007/978-3-032-16365-3_16 | [
"Wes Krikorian",
"Ananya Purwar"
] | # | MICCAI2025 | |
Taking Advantage of MONAI and DiNTS Frameworks to Develop a State-of-the-Art Algorithm for Automatic Segmentation of Brain Metastases | https://doi.org/10.1007/978-3-032-16365-3_17 | [
"Fabian Umeh",
"Nikolay Yordanov",
"Nazanin Maleki",
"Raisa Amiruddin",
"Ahmed W. Moawad",
"Monika Pytlarz",
"Crystal Chukwurah",
"Mariam Aboian"
] | # | MICCAI2025 | |
Ensembling CNN, Transformer, and Mamba with Stacking for Brain Tumor Segmentation | https://doi.org/10.1007/978-3-032-16365-3_18 | [
"Trung Dinh Quoc Dang",
"Huy Hoang Nguyen",
"Aleksei Tiulpin"
] | # | MICCAI2025 | |
SenTumorNet: A Lightweight 3D U-Net Model for Brain Tumor Segmentation in Sub-Saharan African MRI Data | https://doi.org/10.1007/978-3-032-16365-3_19 | [
"Papa Seydou Wane",
"Abdourahamane Balde",
"Guy Mbatchou",
"Dieu-Donné Okalas Ossami",
"Mariama Dione",
"Dieumbe Khoule",
"Mor Diop",
"Ndeye Maty Bousso",
"Adama Traore",
"Aondona M. Iorumbur",
"Raymond Confidence",
"Udunna Anazodo"
] | # | MICCAI2025 | |
TerangaNet: An Optimized 3D U-Net for Brain Tumor Segmentation in Sub-Saharan African MRI Volumes | https://doi.org/10.1007/978-3-032-16365-3_20 | [
"Kéba Faye",
"Abdourahmane Balde",
"Racky Barro Diatta",
"Abdoul Wahab Soumare",
"Penda Ka",
"Mohameth DIA",
"Khoudia Sow",
"Doudou Mohamet Gaye",
"Mohamadou Bamba Diop",
"Magatte Diouf",
"Marième Dieng Fall",
"Guy Mbatchou",
"Aondona M. Iorumbur",
"Raymond Confidence",
"Udunna Anazodo"
... | # | MICCAI2025 | |
EMedNeXt: An Enhanced Brain Tumor Segmentation Framework for Sub-saharan Africa Using MedNeXt V2 with Deep Supervision | https://doi.org/10.1007/978-3-032-16365-3_21 | [
"Ahmed Jaheen",
"Abdelrahman Elsayed",
"Damir Kim",
"Daniil Tikhonov",
"Matheus Scatolin",
"Mohor Banerjee",
"Qiankun Ji",
"Mostafa Salem",
"Hu Wang",
"Sarim Hashmi",
"Mohammad Yaqub"
] | # | MICCAI2025 | |
Improving Pre-trained Adult Glioma Segmentation Models Using only Post-processing Techniques | https://doi.org/10.1007/978-3-032-16365-3_22 | [
"Abhijeet Parida",
"Daniel Capellán-Martín",
"Zhifan Jiang",
"Nishad Kulkarni",
"Krithika Iyer",
"Austin Tapp",
"Syed Muhammad Anwar",
"María J. Ledesma-Carbayo",
"Marius George Linguraru"
] | Gliomas are the most common malignant brain tumors in adults and are among the most lethal. Despite aggressive treatment, the median survival rate is less than 15 months. Accurate multiparametric MRI (mpMRI) tumor segmentation is critical for surgical planning, radiotherapy, and disease monitoring. While deep learning ... | # | MICCAI2025 |
Domain Adaptation for Adult Glioma Segmentation in Sub-Saharan Africa: An Ensemble of nnU-Net v2 and MedNeXt | https://doi.org/10.1007/978-3-032-16365-3_23 | [
"Willem P. E. Boonzaier",
"Farhana Moosa",
"Kagiso Lebang",
"Hanifa Jabaar",
"Aondona M. Iorumbur",
"Dong Zhang",
"Raymond Confidence"
] | # | MICCAI2025 | |
GLIMS-MedNeXt: An Ensemble Framework for Brain MRI Segmentation in Sub-Saharan Africa | https://doi.org/10.1007/978-3-032-16365-3_24 | [
"Ali Azmoudeh",
"Ilkay Öksüz",
"Hazim Kemal Ekenel"
] | # | MICCAI2025 | |
MAPS-Glioma: Modality-Specific Augmentation and Tissue-Adaptive Postprocessing for Robust Glioma Segmentation in Resource-Limited Settings | https://doi.org/10.1007/978-3-032-16365-3_25 | [
"Ayomide B. Oladele",
"Helena Machibya",
"Mariam Kaoneka",
"Frederick Lyimo",
"Debora Hoza",
"Immaculata Kafumu",
"Idris Olalekan",
"Jeremiah Fadugba",
"Dong Zhang",
"Aondona M. Iorumbur",
"Raymond Confidence",
"Nicephorus Rutabasibwa",
"Ugumba M. Kwikima"
] | # | MICCAI2025 | |
BRAIN-CATS: Brain Tumour Reliability-Aware Imaging with Neural Networks Using Calibration-Aware Training and Segmentation | https://doi.org/10.1007/978-3-032-16365-3_26 | [
"Abba Mohammed",
"Zulyadaini Muhammad Aminu",
"Ummulkhairi Ibrahim",
"Amina Suleiman Damo",
"Theodore Barfoot",
"Alexander Hammers",
"Raymond Confidence",
"Aondona M. Iorumbur",
"Abdulrazaq Zubair",
"Mubaraq Yakubu"
] | # | MICCAI2025 | |
How We Won BraTS-SSA 2025: Brain Tumor Segmentation in the Sub-Saharan African Population Using Segmentation-Aware Data Augmentation and Model Ensembling | https://doi.org/10.1007/978-3-032-16365-3_27 | [
"Claudia Takyi Ankomah",
"Livingstone Eli Ayivor",
"Jones Yeboah Nyame",
"Leslie Wambo",
"Patrick Yeboah Bonsu",
"Aondona M. Iorumbur",
"Raymond Confidence",
"Toufiq Musah"
] | # | MICCAI2025 | |
Training Beyond Convergence: Grokking nnU-Net for Glioma Segmentation in Sub-Saharan MRI | https://doi.org/10.1007/978-3-032-16365-3_28 | [
"Mohtady Barakat",
"Omar Salah",
"Ahmed Yasser",
"Mostafa Ahmed",
"Zahirul Arief",
"Waleed Khan",
"Dong Zhang",
"Aondona M. Iorumbur",
"Raymond Confidence",
"Mohannad Barakat",
"Noha Magdy"
] | # | MICCAI2025 | |
Robust Glioblastoma Segmentation Across Multi-modal MRI: A Study on BraTS 2025 Challenge, Task 5 (Sub-Saharan Africa) | https://doi.org/10.1007/978-3-032-16365-3_29 | [
"Abbas Mohamed Rezk",
"Abdulkhalek Al-Fakih",
"Abdullah Shazly",
"Kanghyun Ryu",
"Mohammed A. Al-masni"
] | # | MICCAI2025 | |
Reassessing Glioma Segmentation Strategies: nnU-Net as a Strong Baseline on Limited Sub-Saharan MRI Data | https://doi.org/10.1007/978-3-032-16365-3_30 | [
"Uwimana Lowami",
"Andrew Blayama Stephen",
"Raymond Confidence",
"Dong Zhang",
"Maruf Adewole",
"Udunna C. Anazodo",
"Mehmet Kurt",
"Damilare Olatunji",
"Bernes Lorier Atabonfack"
] | # | MICCAI2025 | |
A Fast, Lightweight nnUNet-Based Brain Tumor Segmentation Model Optimized for Low-Resource African Settings | https://doi.org/10.1007/978-3-032-16365-3_31 | [
"John Emeka",
"Nwokoma Chidiebube",
"Chika Ojiako"
] | # | MICCAI2025 | |
A Self-Supervised Framework for Glioma Segmentation Using Swin UNETR | https://doi.org/10.1007/978-3-032-16365-3_32 | [
"Lesly Tsoptio Fougang",
"Joseph Muthui Wacira",
"Amal Jlassi",
"Dong Zhang",
"Aondona M. Iorumbur",
"Raymond Confidence"
] | # | MICCAI2025 | |
LiMSA-UNet: A Lightweight Modality-Selective Attention ResUNet for Brain-Tumor Segmentation | https://doi.org/10.1007/978-3-032-16365-3_33 | [
"Freedmore Sidume",
"Nkuebe Clement Moleko",
"Botsile Gorata Masalela",
"Preference Mangwayana",
"Lame Kaisara",
"Refilwe Goitsemang",
"Topo Lefika Rapula",
"Dong Zhang",
"Aondona M. Iorumbur",
"Raymond Confidence"
] | # | MICCAI2025 | |
Topology-Driven Fusion of nnU-Net and MedNeXt for Accurate Brain Tumor Segmentation on Sub-saharan Africa Dataset | https://doi.org/10.1007/978-3-032-16365-3_34 | [
"Prabin Bohara",
"Pralhad Kumar Shrestha",
"Arpan Rai",
"Usha Poudel Lamgade",
"Raymond Confidence",
"Dong Zhang",
"Aondona M. Iorumbur",
"Craig Jones",
"Mahesh Shakya",
"Bishesh Khanal",
"Pratibha Kulung"
] | Accurate automatic brain tumor segmentation in Low and Middle-Income (LMIC) countries is challenging due to the lack of defined national imaging protocols, diverse imaging data, extensive use of low-field Magnetic Resonance Imaging (MRI) scanners and limited health-care resources. As part of the Brain Tumor Segmentatio... | # | MICCAI2025 |
Enhancing Pediatric Brain Tumor Segmentation with Attention-Guided 3D U-Net and a Multi-step Tumor-Aware Compositional Augmentation Pipeline in BraTS 2025 | https://doi.org/10.1007/978-3-032-16365-3_35 | [
"Amin Tavallaii",
"Shamim Shah Ghasi"
] | # | MICCAI2025 | |
Enabling Uncertainty Measurement in Multi-subregion Tumor Segmentation: BraTS 2025 Pediatrics | https://doi.org/10.1007/978-3-032-16365-3_36 | [
"Khashayar Namdar",
"Saeidehsadat Mirjalili",
"Sangwook Kim",
"Dominik Deniffel",
"Keith Brunt",
"Leo Anthony Celi",
"Michael D. Cusimano",
"Pascal N. Tyrrell"
] | # | MICCAI2025 | |
Using a Radiologically Informed, Deep Learning Cascade to Refine Segmentations of Pediatric Brain Tumors from MRI | https://doi.org/10.1007/978-3-032-16365-3_37 | [
"Timothy Mulvany",
"Heather Rose",
"Jan Novák",
"Daniel Griffiths-King"
] | # | MICCAI2025 | |
An Advanced nnU-Net Framework for BraTS-2025 PED | https://doi.org/10.1007/978-3-032-16365-3_38 | [
"Xiaolong Li",
"Zhi-Qin John Xu",
"Yan Ren",
"Tianming Qiu",
"Xiaowen Wang"
] | # | MICCAI2025 | |
Memory-Constrained, Noise-Resilient Pediatric Brain Tumor Segmentation via Decoupled Feature Learning and Domain Adaptation - MICCAI BraTS-PEDs 2025 Challenge Solution | https://doi.org/10.1007/978-3-032-16365-3_39 | [
"Meng-Yuan Chen",
"Hsiang-Kuang Tony Liang"
] | # | MICCAI2025 | |
Frequency-Aware Ensemble Learning for BraTS 2025 Pediatric Brain Tumor Segmentation | https://doi.org/10.1007/978-3-032-16365-3_40 | [
"Yuxiao Yi",
"Qingyao Zhuang",
"Zhi-Qin John Xu",
"Xiaowen Wang",
"Yan Ren",
"Tianming Qiu"
] | # | MICCAI2025 | |
Adaptable Segmentation Pipeline for Diverse Brain Tumors with Radiomic-Guided Subtyping and Lesion-Wise Model Ensemble | https://doi.org/10.1007/978-3-032-16365-3_41 | [
"Daniel Capellán-Martín",
"Abhijeet Parida",
"Zhifan Jiang",
"Nishad Kulkarni",
"Krithika Iyer",
"Austin Tapp",
"Syed Muhammad Anwar",
"María J. Ledesma-Carbayo",
"Marius George Linguraru"
] | Robust and generalizable segmentation of brain tumors on multi-parametric magnetic resonance imaging (MRI) remains difficult because tumor types differ widely. The BraTS 2025 Lighthouse Challenge benchmarks segmentation methods on diverse high-quality datasets of adult and pediatric tumors: multi-consortium internation... | # | MICCAI2025 |
A Multitask Learning Approach for Segmenting Brain Tumor Sub-regions: Towards Better Generalization | https://doi.org/10.1007/978-3-032-16365-3_42 | [
"Mumu Aktar",
"Tasneem Nasser",
"Roberto Souza"
] | # | MICCAI2025 | |
BraTS-FL: Enhancing Generalization in Brain Tumor Segmentation via Federated Learning | https://doi.org/10.1007/978-3-032-16365-3_43 | [
"Simone Bendazzoli",
"Rodrigo Moreno"
] | # | MICCAI2025 | |
Scaling High-Capacity ResUNet with Dynamic Batch for Universal Brain Tumor Segmentation - A BraTS 2025 "Generalizable to All Tumors" (GoAT) Challenge Solution | https://doi.org/10.1007/978-3-032-16365-3_44 | [
"Meng-Yuan Chen",
"Hsiang-Kuang Tony Liang"
] | # | MICCAI2025 | |
Towards Label-Free Brain Tumor Segmentation: Unsupervised Learning with Multimodal MRI | https://doi.org/10.1007/978-3-032-16365-3_45 | [
"Gerard Comas-Quiles",
"Carles García-Cabrera",
"Julia Dietlmeier",
"Noel E. O'Connor",
"Ferran Marqués"
] | # | MICCAI2025 | |
ADMFNet: Enhancing Cross-Tumor Generalization in Multi-Modal MRI Segmentation | https://doi.org/10.1007/978-3-032-16365-3_46 | [
"Hongjuan Wang",
"Yixin Zhang",
"Jindong Sun",
"Xinjun An",
"Liying Zhu",
"Chunyao Li"
] | # | MICCAI2025 | |
Enhancing Brain Tumor Segmentation Generalizability via Pseudo-Labeling and Ratio-Adaptive Postprocessing | https://doi.org/10.1007/978-3-032-16365-3_47 | [
"To-Liang Hsu",
"Dang Khoa Nguyen",
"Pai Lin",
"Ching-Ting Lin",
"Wei-Chun Wang"
] | # | MICCAI2025 | |
Ensemble-Based Generalization for Brain Tumor Segmentation Using NnU-Net Variants and Swin UNETR | https://doi.org/10.1007/978-3-032-16365-3_48 | [
"Vaidehi Satushe",
"Madhav Arora",
"Vibha Vyas",
"Shilpa Metkar"
] | # | MICCAI2025 | |
Medical Image Computing and Computer Assisted Intervention - MICCAI 2025 - 28th International Conference, Daejeon, South Korea, September 23-27, 2025, Proceedings, Part XVI | https://doi.org/10.1007/978-3-032-05325-1 | [
"James C. Gee",
"Daniel C. Alexander",
"Jaesung Hong",
"Juan Eugenio Iglesias",
"Carole H. Sudre",
"Archana Venkataraman",
"Polina Golland",
"Jong Hyo Kim",
"Jinah Park"
] | # | MICCAI2025 | |
3D Acetabular Surface Reconstruction from 2D Pre-operative X-Ray Images Using SRVF Elastic Registration and Deformation Graph | https://doi.org/10.1007/978-3-032-05325-1_1 | [
"Shuai Zhang",
"Jinliang Wang",
"Xu Wang",
"Sujith Konandetails",
"Danail Stoyanov",
"Evangelos B. Mazomenos"
] | Accurate and reliable selection of the appropriate acetabular cup size is crucial for restoring joint biomechanics in total hip arthroplasty (THA). This paper proposes a novel framework that integrates square-root velocity function (SRVF)-based elastic shape registration technique with an embedded deformation (ED) grap... | # | MICCAI2025 |
A Boundary-Aware Cold-Diffusion Model for Electron Microscopy Segmentation | https://doi.org/10.1007/978-3-032-05325-1_2 | [
"Muge Qi",
"Ruohua Shi",
"Yu Cai",
"Liuyuan He",
"Wenyao Wang",
"Lei Ma"
] | # | MICCAI2025 | |
A Virtual Domain Collaborative Learning Framework for Semi-supervised Microscopic Hyperspectral Image Segmentation | https://doi.org/10.1007/978-3-032-05325-1_3 | [
"Geng Qin",
"Huan Liu",
"Wei Li",
"Haihao Zhang",
"Yuxing Guo"
] | # | MICCAI2025 | |
ADAptation: Reconstruction-Based Unsupervised Active Learning for Breast Ultrasound Diagnosis | https://doi.org/10.1007/978-3-032-05325-1_4 | [
"Yaofei Duan",
"Yuhao Huang",
"Xin Yang",
"Luyi Han",
"Xinyu Xie",
"Zhiyuan Zhu",
"Ping He",
"Ka-Hou Chan",
"Ligang Cui",
"Sio Kei Im",
"Dong Ni",
"Tao Tan"
] | # | MICCAI2025 | |
Adapting Foundation Model for Dental Caries Detection with Dual-View Co-training | https://doi.org/10.1007/978-3-032-05325-1_5 | [
"Tao Luo",
"Han Wu",
"Tong Yang",
"Dinggang Shen",
"Zhiming Cui"
] | Accurate dental caries detection from panoramic X-rays plays a pivotal role in preventing lesion progression. However, current detection methods often yield suboptimal accuracy due to subtle contrast variations and diverse lesion morphology of dental caries. In this work, inspired by the clinical workflow where dentist... | https://github.com/ShanghaiTech-IMPACT/DVCTNet | MICCAI2025 |
AdvMIM: Adversarial Masked Image Modeling for Semi-supervised Medical Image Segmentation | https://doi.org/10.1007/978-3-032-05325-1_6 | [
"Lei Zhu",
"Jun Zhou",
"Rick Siow Mong Goh",
"Yong Liu"
] | Vision Transformer has recently gained tremendous popularity in medical image segmentation task due to its superior capability in capturing long-range dependencies. However, transformer requires a large amount of labeled data to be effective, which hinders its applicability in annotation scarce semi-supervised learning... | https://github.com/zlheui/AdvMIM | MICCAI2025 |
All-in-One Medical Image Restoration with Latent Diffusion-Enhanced Vector-Quantized Codebook Prior | https://doi.org/10.1007/978-3-032-05325-1_7 | [
"Haowei Chen",
"Zhiwen Yang",
"Haotian Hou",
"Hui Zhang",
"Bingzheng Wei",
"Gang Zhou",
"Yan Xu"
] | All-in-one medical image restoration (MedIR) aims to address multiple MedIR tasks using a unified model, concurrently recovering various high-quality (HQ) medical images (e.g., MRI, CT, and PET) from low-quality (LQ) counterparts. However, all-in-one MedIR presents significant challenges due to the heterogeneity across... | # | MICCAI2025 |
AVDM: Controllable Adversarial Diffusion Model for Vessel-to-Volume Synthesis | https://doi.org/10.1007/978-3-032-05325-1_8 | [
"Jian Dai",
"Wanchen Liu",
"Honghao Cui",
"Xiao Liu",
"Jiajun Wang",
"Zhiji Zheng",
"Daoying Geng"
] | # | MICCAI2025 | |
Bowsher Prior Enhanced Unsupervised PET Image Denoising | https://doi.org/10.1007/978-3-032-05325-1_9 | [
"Zhongxue Wu",
"Jiankai Wu",
"Jianan Cui",
"Yuanjing Feng",
"Zan Chen"
] | # | MICCAI2025 | |
BraTS-UMamba: Adaptive Mamba UNet with Dual-Band Frequency Based Feature Enhancement for Brain Tumor Segmentation | https://doi.org/10.1007/978-3-032-05325-1_10 | [
"Haoran Yao",
"Hao Xiong",
"Dong Liu",
"Hualei Shen",
"Shlomo Berkovsky"
] | # | MICCAI2025 | |
Compact Training-Free NAS with Alternating Evolution Game for Medical Image Segmentation | https://doi.org/10.1007/978-3-032-05325-1_11 | [
"Xiao-Xue Sun",
"Hongpeng Wang",
"Pei-Cheng Song"
] | # | MICCAI2025 | |
Controllable Flow Matching for 3D Contrast-Enhanced Brain MRI Synthesis from Non-contrast Scans | https://doi.org/10.1007/978-3-032-05325-1_12 | [
"Heng Chang",
"Yu Shang",
"Haifeng Wang",
"Yuxia Liang",
"Haoyu Wang",
"Fan Wang",
"Chen Niu",
"Chunfeng Lian"
] | # | MICCAI2025 | |
Controllable Skin Synthesis via Lesion-Focused Vector Autoregression Model | https://doi.org/10.1007/978-3-032-05325-1_13 | [
"Jiajun Sun",
"Zhen Yu",
"Siyuan Yan",
"Jason J. Ong",
"Zongyuan Ge",
"Lei Zhang"
] | Skin images from real-world clinical practice are often limited, resulting in a shortage of training data for deep-learning models. While many studies have explored skin image synthesis, existing methods often generate low-quality images and lack control over the lesion's location and type. To address these limitations... | https://github.com/echosun1996/LF-VAR | MICCAI2025 |
Cross-View Generalized Diffusion Model for Sparse-View CT Reconstruction | https://doi.org/10.1007/978-3-032-05325-1_14 | [
"Jixiang Chen",
"Yiqun Lin",
"Yi Qin",
"Hualiang Wang",
"Xiaomeng Li"
] | Sparse-view computed tomography (CT) reduces radiation exposure by subsampling projection views, but conventional reconstruction methods produce severe streak artifacts with undersampled data. While deep-learning-based methods enable single-step artifact suppression, they often produce over-smoothed results under signi... | https://github.com/xmed-lab/CvG-Diff | MICCAI2025 |
D3M: Deformation-Driven Diffusion Model for Synthesis of Contrast-Enhanced MRI with Brain Tumors | https://doi.org/10.1007/978-3-032-05325-1_15 | [
"Haowen Pang",
"Peng Zhang",
"Xiaoming Hong",
"Shannan Chen",
"Chuyang Ye"
] | # | MICCAI2025 | |
DiffAtlas: GenAI-Fying Atlas Segmentation via Image-Mask Diffusion | https://doi.org/10.1007/978-3-032-05325-1_16 | [
"Hantao Zhang",
"Yuhe Liu",
"Jiancheng Yang",
"Weidong Guo",
"Xinyuan Wang",
"Pascal Fua"
] | Accurate medical image segmentation is crucial for precise anatomical delineation. Deep learning models like U-Net have shown great success but depend heavily on large datasets and struggle with domain shifts, complex structures, and limited training samples. Recent studies have explored diffusion models for segmentati... | https://github.com/m3dv/diffatlas | MICCAI2025 |
Diffusion-Based Multi-modal MR Fusion for TOF-MRA Image Synthesis | https://doi.org/10.1007/978-3-032-05325-1_17 | [
"Tianen Yu",
"Xinyu Song",
"Lei Xiang",
"Tao Zhou"
] | # | MICCAI2025 | |
DuoDent: Tooth Generation Using Dual-Stream Diffusion with Normal Consistency | https://doi.org/10.1007/978-3-032-05325-1_18 | [
"Doeyoung Kwon",
"Seongjun Kim",
"In-Seok Song",
"Seung Jun Baek"
] | # | MICCAI2025 | |
EFFDNet: A Scribble-Supervised Medical Image Segmentation Method with Enhanced Foreground Feature Discrimination | https://doi.org/10.1007/978-3-032-05325-1_19 | [
"Jinhua Liu",
"Shu Yun Tan",
"Xulei Yang",
"Yanwu Xu",
"Si Yong Yeo"
] | # | MICCAI2025 | |
FilterDiff: Noise-Free Frequency-Domain Diffusion Models for Accelerated MRI Reconstruction | https://doi.org/10.1007/978-3-032-05325-1_20 | [
"Tao Song",
"Fang Nie",
"Yi Guo",
"Feng Xu",
"Shaoting Zhang"
] | # | MICCAI2025 | |
Flow Matching for Medical Image Synthesis: Bridging the Gap Between Speed and Quality | https://doi.org/10.1007/978-3-032-05325-1_21 | [
"Milad Yazdani",
"Yasamin Medghalchi",
"Pooria Ashrafian",
"Ilker Hacihaliloglu",
"Dena Shahriari"
] | Deep learning models have emerged as a powerful tool for various medical applications. However, their success depends on large, high-quality datasets that are challenging to obtain due to privacy concerns and costly annotation. Generative models, such as diffusion models, offer a potential solution by synthesizing medi... | https://github.com/milad1378yz/motfm | MICCAI2025 |
FMM-Diff: A Feature Mapping and Merging Diffusion Model for MRI Generation with Missing Modality | https://doi.org/10.1007/978-3-032-05325-1_22 | [
"Wenjin Zhong",
"Cong Cong",
"Zihan Wang",
"Zeya Yan",
"Antonio Di Ieva",
"Sidong Liu"
] | # | MICCAI2025 | |
GLCP: Global-to-Local Connectivity Preservation for Tubular Structure Segmentation | https://doi.org/10.1007/978-3-032-05325-1_23 | [
"Feixiang Zhou",
"Zhuangzhi Gao",
"He Zhao",
"Jianyang Xie",
"Yanda Meng",
"Yitian Zhao",
"Gregory Yoke Hong Lip",
"Yalin Zheng"
] | # | MICCAI2025 | |
Guiding Quantitative MRI Reconstruction with Phase-Wise Uncertainty | https://doi.org/10.1007/978-3-032-05325-1_24 | [
"Haozhong Sun",
"Zhongsen Li",
"Chenlin Du",
"Haokun Li",
"Yajie Wang",
"Huijun Chen"
] | Quantitative magnetic resonance imaging (qMRI) requires multi-phase acqui-sition, often relying on reduced data sampling and reconstruction algorithms to accelerate scans, which inherently poses an ill-posed inverse problem. While many studies focus on measuring uncertainty during this process, few explore how to lever... | # | MICCAI2025 |
High-Fidelity Unified One-to-Many Medical Image Synthesis via Text-Conditioned Latent Diffusion | https://doi.org/10.1007/978-3-032-05325-1_25 | [
"Youjian Zhang",
"Jian Huang",
"Jie Wang",
"Zezhou Li",
"Zhongya Wang",
"Guanqun Zhou",
"Zhicheng Zhang",
"Gang Yu"
] | # | MICCAI2025 | |
Improving Medical Image Segmentation with Implicit Representation and Noisy Label Robustness | https://doi.org/10.1007/978-3-032-05325-1_26 | [
"Suruchi Kumari",
"Harshdeep Singh",
"Pravendra Singh"
] | # | MICCAI2025 |
End of preview. Expand in Data Studio
PaperVault Dataset · 论文元数据库
🔎 项目简介 · Overview
PaperVault 是一份持续自动更新的统一论文元数据库，覆盖自然语言处理、计算机视觉、机器学习、数据挖掘、数据库、语音、系统、网络、安全、理论计算机科学、人机交互、计算机图形学与多媒体等方向的顶级会议与期刊。PaperVault is a continuously-updated, unified metadata database of papers from top-tier conferences and journals across NLP, Computer Vision, Machine Learning, Data Mining, Databases, Speech, Systems, Networking, Security, Theory, HCI, Graphics, and Multimedia.
🌐 源仓库 / Source: github.com/youngfish42/PaperVault — Web UI、REST API、采集流水线、Issues / PRs 全部在那里 · Web UI, REST API, crawling pipelines and issues/PRs all live there.
🌐 在线搜索 / Live search: papervault.top — 直接使用 Web 检索界面，无需下载数据 · Use the web search UI directly without downloading the dataset.
🆕 最近更新 · Recent Update
- 📅 最近更新 · Last updated: 2026-10-05 (Asia/Shanghai)
- 📊 数据库规模 · Database size: 713,899 篇论文 / 129 个刊物系列 / 624,840 篇含摘要 / 112,009 篇含开源代码（713,899 papers / 129 venue series / 624,840 with abstract / 112,009 with code）
📈 数据看板 · Statistics at a Glance
下列 4 张统计图与 cache.jsonl.gz 同源同步，反映本数据集的最新状态。The four charts below are generated from the same cache.jsonl.gz and always reflect the latest state of this dataset.
📦 数据集内容 · What's in this dataset
| 路径 Path | 子集 Subset | 格式 Format | 说明 Description |
|---|---|---|---|
cache/cache.jsonl.gz |
papers（默认 / default） |
gzip-compressed JSON Lines (UTF-8) | 每行一篇论文 · One paper per line; one JSON object per line |
cache/abstract_backfill_progress.jsonl.gz |
abstract_backfill_progress |
gzip-compressed JSON Lines (UTF-8) | 摘要回填流水线的进度/断点记录，不是论文元数据；仅供工作流恢复使用 · Append-only progress log of the abstract-backfill pipeline (not paper records); used by the workflow to resume between runs |
Hugging Face 会自动为 cache.jsonl.gz 生成 Parquet 视图，也可直接用 datasets.load_dataset(...) 读取，无需手动解压。Hugging Face also exposes an auto-generated Parquet view, so datasets.load_dataset(...) works out of the box.
💡 Dataset Viewer 与
datasets.load_dataset("youngfish42/PaperVault")默认展示/加载的都是papers子集（即cache/cache.jsonl.gz）。如需查看回填进度，请在 Viewer 顶部下拉框切换到abstract_backfill_progress，或调用load_dataset("youngfish42/PaperVault", name="abstract_backfill_progress")。The Dataset Viewer anddatasets.load_dataset("youngfish42/PaperVault")both default to thepaperssubset (cache/cache.jsonl.gz). To inspect backfill progress, switch the Viewer's subset dropdown toabstract_backfill_progressor callload_dataset("youngfish42/PaperVault", name="abstract_backfill_progress").
📐 字段 Schema
| Field 字段 | Type 类型 | Notes 说明 |
|---|---|---|
paper_name |
string | 论文标题（已归一化）· Normalised paper title |
paper_authors |
list[string] | 作者列表，按原始顺序 · Author names in order |
paper_url |
string | 论文在原始平台的链接（PDF 或落地页）· Canonical URL on the venue's site (PDF or landing page) |
paper_abstract |
string | 摘要；未回填时为空字符串 · Abstract; may be empty when not yet backfilled |
paper_code |
string | 从摘要中抽取出的 GitHub 仓库 URL；"#" 是「未发现代码链接」的占位符 · GitHub repository URL extracted from the abstract; "#" is the sentinel for "no code link discovered" |
conf |
string | 会议+年份标识，如 ACL2024、NIPS2023、CVPR2025；去掉末尾四位数字即可得到会议系列。注意 NeurIPS Proceedings 沿用历史命名 NIPS{year}。Venue + year identifier (e.g. ACL2024, NIPS2023, CVPR2025). Strip the trailing 4-digit year to recover the venue series. Note that NeurIPS Proceedings entries use the historical name NIPS{year}. |
缺失字段请按空字符串处理。Treat missing fields as empty strings.
⬇️ 获取方式 · How to download
下面三种方式任选其一即可，无需克隆 GitHub 仓库。Pick any one of the three options below — no GitHub clone is required.
方式 A · Option A — huggingface_hub（推荐 / recommended）
from huggingface_hub import hf_hub_download
import gzip, json
path = hf_hub_download(
repo_id="youngfish42/PaperVault",
filename="cache/cache.jsonl.gz",
repo_type="dataset",
)
with gzip.open(path, "rt", encoding="utf-8") as f:
for line in f:
record = json.loads(line)
# 在这里处理一条记录 · do something with the record
方式 B · Option B — datasets
from datasets import load_dataset
ds = load_dataset("youngfish42/PaperVault")
print(ds[next(iter(ds))][0])
数据集只有一个默认 split（非 ML 训练集），不要传
split="train"。Single default split — do not passsplit="train".
方式 C · Option C — huggingface-cli / 直接 HTTPS · Plain HTTPS
huggingface-cli download youngfish42/PaperVault \
cache/cache.jsonl.gz --repo-type dataset --local-dir ./data
💡 文件压缩后约 120 MB（会随数据持续增长），解压后是 GB 级 JSONL 流，请按行流式读取，不要整体载入内存。The file is ~120 MB compressed (and growing) and decompresses to a multi-GB JSONL stream. Stream it line-by-line rather than loading the whole thing into memory.
🔁 更新节奏 · Update cadence
数据集由三个 GitHub Actions 工作流负责重建并推送到本 Hub 仓库 / The dataset is rebuilt and pushed to this Hub repo by three GitHub Actions workflows：
| 工作流 Workflow | 触发节奏 Schedule | 推送的内容 What it pushes |
|---|---|---|
collect_papers |
每月 15 号 16:00 UTC + 手动触发 · 15th of every month at 16:00 UTC + workflow_dispatch |
增量抓取新发现的会议/年份组合 · Incremental crawl of newly-discovered conference/year combinations |
backfill_abstracts |
每月 1 号 00:00 UTC + 手动触发 · 1st of every month at 00:00 UTC + workflow_dispatch |
为已有论文回填 paper_abstract · Adds paper_abstract for papers that were collected without one |
update_readme |
仅手动触发 (workflow_dispatch) · Manual only (workflow_dispatch) |
默认仅刷新 README 与统计；当输入参数 mode=force 时执行全量重建 · Refreshes the README and statistics by default; performs a full rebuild only when invoked with mode=force |
每次推送都使用 Hugging Face 的 parent_commit 乐观锁机制，避免并发覆盖。Each push uses Hugging Face's parent_commit optimistic-lock mechanism to avoid silently overwriting concurrent updates.
🌐 线上服务 · Live Service
无需下载数据集即可直接体验 Web 检索： No need to download the dataset — try the web search directly:
🔗 关联仓库 · Related repository
如果你需要完整的搜索 Web UI（智能搜索 + Web of Science 风格的高级查询 DSL）、REST API（/api/v1/*）、抓取 / 合并 / 摘要回填流水线源码、收录会议范围、统计仪表盘、项目截图或贡献指南，请前往 GitHub 项目仓库。If you are looking for the full search Web UI (smart search + Web-of-Science-style advanced query DSL), the REST API (/api/v1/*), the crawling / merging / abstract-backfill pipelines source code, conference coverage, statistics dashboards, screenshots or contribution guidelines, please visit the GitHub repository.
👉 github.com/youngfish42/PaperVault
📜 许可证 · License
代码以 GPL-3.0 发布；每条论文记录的著作权仍属于原作者 / 出版方，本数据集仅重新分发公开可获取的元数据与链接。Code is released under GPL-3.0; individual paper records remain the IP of their authors/publishers — this dataset only redistributes publicly available bibliographic metadata and links.
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