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+---
+library_name: pytorch
+pipeline_tag: image-segmentation
+license: apache-2.0
+datasets:
+  - ksanchez84/LUTSeg
+tags:
+  - medical
+  - semantic-segmentation
+  - semi-supervised-learning
+  - dinov2
+  - medsiglip
+---
+
+# TiSage Segmentation Checkpoints
+
+Paper checkpoints for **TiSage: Tissue Segmentation with Multi-Scale Semantic
+Guidance**, released with the code and reproducibility material at
+[carlosh93/TiSage](https://github.com/carlosh93/TiSage). The paper was selected
+as a **Spotlight** at the Eleventh ISIC Skin Image Analysis Workshop @ MICCAI
+2026.
+
+## Files
+
+| Checkpoint | Dataset | Method | Paper setting |
+|---|---|---|---|
+| `lutseg_tisage_1_8_seed0_best.pth` | LUTSeg | TiSage | 1/8 labeled, seed 0 |
+| `lutseg_unimatch_v2_1_8_seed0_best.pth` | LUTSeg | UniMatch-V2 | 1/8 labeled, seed 0 |
+| `dfutissue_tisage_fixed_seed2_best.pth` | DFUTissue | TiSage | fixed/full supervision split, seed 2 |
+| `dfutissue_unimatch_v2_fixed_seed1_best.pth` | DFUTissue | UniMatch-V2 | fixed/full supervision split, seed 1 |
+
+Each file contains `model`, `model_ema`, optimizer state, epoch, and validation
+selection metadata. TiSage evaluation defaults to `model_ema`; pass an explicit
+state to the evaluator when comparing student and teacher weights. The LUTSeg
+pair is also used by the repository's Figure 4 reproduction script.
+
+## Download
+
+```python
+from huggingface_hub import hf_hub_download
+
+checkpoint = hf_hub_download(
+    repo_id="ksanchez84/TiSage",
+    filename="lutseg_tisage_1_8_seed0_best.pth",
+    local_dir="method/checkpoints/downloaded",
+)
+print(checkpoint)
+```
+
+Download every released checkpoint with:
+
+```python
+from huggingface_hub import snapshot_download
+
+snapshot_download(
+    repo_id="ksanchez84/TiSage",
+    local_dir="method/checkpoints/downloaded",
+)
+```
+
+Verify the files with `segmentation_checkpoints.sha256` from this model
+repository or the TiSage code repository.
+
+## Evaluation
+
+Clone the [TiSage repository](https://github.com/carlosh93/TiSage), install its
+requirements, download LUTSeg, and run:
+
+```bash
+python method/eval/evaluate_checkpoint.py \
+  --config method/configs/tisage_lutseg.yaml \
+  --checkpoint method/checkpoints/downloaded/lutseg_tisage_1_8_seed0_best.pth
+```
+
+The public LUTSeg release has a patient-disjoint validation set and no separate
+public test split. The evaluator reports per-class IoU and Dice plus their
+means on the 30-image validation set.
+
+## Architecture and Training
+
+The segmentation network is a DINOv2-Base DPT model. TiSage trains it in an EMA
+teacher-student framework using frozen MedSigLIP semantic guidance and the
+small dataset-specific prior heads committed in the code repository. MedSigLIP
+parameters are not included in these checkpoint files.
+
+Exact configurations, selected seeds, launch commands, result evidence, and
+the bounded training check are maintained in the TiSage repository. LUTSeg is
+available at [ksanchez84/LUTSeg](https://huggingface.co/datasets/ksanchez84/LUTSeg).
+
+## Intended Use
+
+These checkpoints support research reproduction and non-clinical exploration
+of wound-tissue segmentation. They are not medical devices and must not be used
+alone for diagnosis or treatment. Results may not transfer to other patient
+populations, institutions, cameras, wound etiologies, or acquisition settings.
+
+## License and Attribution
+
+The checkpoint release is distributed under Apache License 2.0 because it
+contains DINOv2-derived backbone parameters. TiSage code remains MIT licensed,
+and datasets retain their own terms. Training used the gated
+[`google/medsiglip-448`](https://huggingface.co/google/medsiglip-448) model as a
+frozen prior; its parameters are not redistributed here. See `NOTICE.md` for
+the complete attribution and scope.
+
+## Citation
+
+Please cite *LUTSeg: A Longitudinal Multi-Expert Dataset for Ulcer Tissue
+Segmentation*, Eleventh ISIC Skin Image Analysis Workshop @ MICCAI 2026. Final
+proceedings metadata will be added when the bibliographic record is public.