| Third-party material redistributed in this repository |
| ===================================================== |
|
|
| Every model here is a TorchScript export of a pretrained network published by someone else. The |
| weights are unchanged; what this repository adds is a wrapper that normalises the input from the |
| statistics IMPACT passes and returns one tensor per feature layer. The export scripts are published in |
| https://github.com/vboussot/ImpactLoss under Data/Models/builds/. |
|
|
| Anatomix/Anatomix.pt, Anatomix/AnatomixDevViT.pt |
| ------------------------------------------------ |
| Weights: https://huggingface.co/neeldey/anatomix (anatomix.pth, anatomix-dev-vit.pth) |
| Code: https://github.com/neel-dey/anatomix |
| License: MIT, Copyright 2024 Neel Dey -- LICENSES/anatomix-MIT.txt |
| Paper: Dey et al., Learning General-purpose Biomedical Volume Representations using Randomized |
| Synthesis, ICLR 2025, arXiv:2411.02372 |
|
|
| AnatomixDevViT additionally builds on PrimusV2 from dynamic-network-architectures, which anatomix |
| configures and extends (output normalisation, register-token initialisation, QK normalisation): |
|
|
| Code: https://github.com/MIC-DKFZ/dynamic-network-architectures |
| License: Apache License 2.0, Copyright 2022 Division of Medical Image Computing, German Cancer |
| Research Center (DKFZ) -- LICENSES/dynamic-network-architectures-Apache-2.0.txt |
|
|
| TS/M258.pt, TS/M291-M295.pt, TS/M297-M298.pt, TS/M730-M733.pt, TS/M850-M853.pt |
| ------------------------------------------------------------------------------- |
| Weights: TotalSegmentator, https://github.com/wasserth/TotalSegmentator |
| License: Apache License 2.0 -- LICENSES/Apache-2.0.txt |
| Paper: Wasserthal et al., TotalSegmentator: robust segmentation of 104 anatomic structures in CT |
| images, Radiology: Artificial Intelligence, 2023, arXiv:2208.05868 |
|
|
| TotalSegmentator publishes its tasks under two different terms. The models here come from the tasks |
| it offers under Apache 2.0: `total` (M291-M295, M297, M298), `total_mr` (M730-M733 in the 2.2/2.4 |
| series, M850-M853 since) and `lung_vessels` (M258). Its other tasks -- heartchambers_highres, |
| tissue_types, brain_structures, appendicular_bones, face, thigh_shoulder_muscles, coronary_arteries |
| and the rest of that list -- need a license number from the authors, free for non-commercial use and |
| paid otherwise, and their weights must NOT be redistributed here. |
|
|
| MRSeg/MRSeg.pt |
| -------------- |
| Weights: MRSegmentator, https://github.com/hhaentze/MRSegmentator |
| License: Apache License 2.0 -- LICENSES/Apache-2.0.txt |
| Paper: Haentze et al., MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT, |
| 2024, arXiv:2405.06463 |
|
|
| SAM2.1/SAM2.1_Small.pt, SAM2.1/SAM2.1_Tiny.pt |
| ---------------------------------------------- |
| Weights: SAM 2, https://github.com/facebookresearch/sam2 |
| License: Apache License 2.0, Copyright (c) Meta Platforms, Inc. and affiliates -- |
| LICENSES/Apache-2.0.txt |
| Paper: Ravi et al., SAM 2: Segment Anything in Images and Videos, 2024, arXiv:2408.00714 |
|
|
| Dino/DinoV2_Small.pt |
| -------------------- |
| Weights: DINOv2, https://github.com/facebookresearch/dinov2 |
| License: Apache License 2.0, Copyright (c) Meta Platforms, Inc. and affiliates -- |
| LICENSES/Apache-2.0.txt |
| Paper: Oquab et al., DINOv2: Learning Robust Visual Features without Supervision, 2023, |
| arXiv:2304.07193 |
|
|
| DINOv3 is NOT redistributed here: its checkpoints fall under the DINOv3 License Agreement rather than |
| an open-source license. The export script in ImpactLoss builds them locally from your own download. |
|
|
| VGG/VGG16.pt |
| ------------ |
| Weights: torchvision, https://github.com/pytorch/vision |
| License: BSD 3-Clause, Copyright (c) Soumith Chintala 2016 -- |
| LICENSES/torchvision-BSD-3-Clause.txt |
| Paper: Simonyan and Zisserman, Very Deep Convolutional Networks for Large-Scale Image |
| Recognition, 2014, arXiv:1409.1556 |
|
|
| MIND/*.pt |
| --------- |
| No third-party weights: MIND is a handcrafted descriptor, implemented here from its paper. |
| Paper: Heinrich et al., MIND: Modality independent neighbourhood descriptor for multi-modal |
| deformable registration, Medical Image Analysis, 2012, doi:10.1016/j.media.2012.05.008 |
|
|
