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 |
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