We lifted 13 functions out of this paper's own repositories and ran 9 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| OpenGVLab/SAM-Med2D | canonical | 9 of 13 |
| Function | Status | Where it lives |
|---|---|---|
| SegMetrics | Ran | OpenGVLab/SAM-Med2D/metrics.py code served (permissive licence) · get_code("757040dbafc5b5a8") |
| dice | Ran | OpenGVLab/SAM-Med2D/metrics.py code served (permissive licence) · get_code("3c2ecf169a81938f") |
| get_boxes_from_mask | Ran | OpenGVLab/SAM-Med2D/utils.py code served (permissive licence) · get_code("69b17f3689f7b247") |
| get_rel_pos | Ran | OpenGVLab/SAM-Med2D/segment_anything/modeling/image_encoder.py code served (permissive licence) · get_code("733d7f0bedcb74c2") |
| init_point_sampling | Ran | OpenGVLab/SAM-Med2D/utils.py code served (permissive licence) · get_code("89a12fc455e2113d") |
| iou | Ran | OpenGVLab/SAM-Med2D/metrics.py code served (permissive licence) · get_code("5f1cca6dc655957a") |
| select_random_points | Ran | OpenGVLab/SAM-Med2D/utils.py code served (permissive licence) · get_code("b6a85575a3999468") |
| stack_dict_batched | Ran | OpenGVLab/SAM-Med2D/DataLoader.py code served (permissive licence) · get_code("32e8a89b26e59d49") |
| window_partition | Ran | OpenGVLab/SAM-Med2D/segment_anything/modeling/image_encoder.py code served (permissive licence) · get_code("105fa08885dc36cc") |
| build_sam_vit_b | Not yet run | OpenGVLab/SAM-Med2D/segment_anything/build_sam.py code served (permissive licence) · get_code("c79820579e477987") |
| build_sam_vit_h | Not yet run | OpenGVLab/SAM-Med2D/segment_anything/build_sam.py code served (permissive licence) · get_code("61dc61b4f44c36d2") |
| build_sam_vit_l | Not yet run | OpenGVLab/SAM-Med2D/segment_anything/build_sam.py code served (permissive licence) · get_code("3fb98a1ac23c9741") |
| window_unpartition | Not yet run | OpenGVLab/SAM-Med2D/segment_anything/modeling/image_encoder.py code served (permissive licence) · get_code("27be441cc8213e52") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Segment Anything Model (SAM) has achieved impressive results for natural image segmentation with input prompts such as points and bounding boxes. Its success largely owes to massive labeled training data. However, directly applying SAM to medical image segmentation cannot perform well because SAM lacks medical knowledge -- it does not use medical images for training. To incorporate medical knowledge into SAM, we introduce SA-Med2D-20M, a large-scale segmentation dataset of 2D medical images built upon numerous public and private datasets. It consists of 4.6 million 2D medical images and 19.7 million corresponding masks, covering almost the whole body and showing significant diversity. This paper describes all the datasets collected in SA-Med2D-20M and details how to process these datasets. Furthermore, comprehensive statistics of SA-Med2D-20M are presented to facilitate the better use of our dataset, which can help the researchers build medical vision foundation models or apply their models to downstream medical applications. We hope that the large scale and diversity of SA-Med2D-20M can be leveraged to develop medical artificial intelligence for enhancing diagnosis, medical image analysis, knowledge sharing, and education. The data with the redistribution license is publicly available at https://github.com/OpenGVLab/SAM-Med2D.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2311.11969")
get_code_for_paper("2311.11969")
have("2311.11969")
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