We lifted 3 functions out of this paper's own repositories and ran 2 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 |
|---|---|---|
| zhaoziheng/sat-ds | canonical | 2 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| Normalization | Ran | zhaoziheng/sat-ds/loader.py pointer only (licence: NONE) · get_code("a6bc7496b6b250f9") |
| contains | Ran | zhaoziheng/sat-ds/train_test_split.py pointer only (licence: NONE) · get_code("a518e29963d7ed31") |
| npy_loader | Not yet run | zhaoziheng/sat-ds/loader_npy.py pointer only (licence: NONE) · get_code("321b45a28a2354f6") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
This paper aims to build a model that can Segment Anything in 3D medical images, driven by medical terminologies as Text prompts, termed as SAT. Our main contributions are three-fold: (i) We construct the first multimodal knowledge tree on human anatomy, including 6502 anatomical terminologies; Then, we build the largest and most comprehensive segmentation dataset for training, collecting over 22K 3D scans from 72 datasets, across 497 classes, with careful standardization on both image and label space; (ii) We propose to inject medical knowledge into a text encoder via contrastive learning and formulate a large-vocabulary segmentation model that can be prompted by medical terminologies in text form; (iii) We train SAT-Nano (110M parameters) and SAT-Pro (447M parameters). SAT-Pro achieves comparable performance to 72 nnU-Nets -- the strongest specialist models trained on each dataset (over 2.2B parameters combined) -- over 497 categories. Compared with the interactive approach MedSAM, SAT-Pro consistently outperforms across all 7 human body regions with +7.1% average Dice Similarity Coefficient (DSC) improvement, while showing enhanced scalability and robustness. On 2 external (cross-center) datasets, SAT-Pro achieves higher performance than all baselines (+3.7% average DSC), demonstrating superior generalization ability.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2312.17183")
get_code_for_paper("2312.17183")
have("2312.17183")
Connect an agent — have() is free.