CRC ESD

--- license: mit tags: - computational-pathology - colorectal-cancer - multiple-instance-learning - whole-slide-image - medical-imaging language: en pipeline_tag: image-classification --- # CRC-ESD gated ABMIL checkpoints This repository provides the five patient-level cross-validation checkpoints for research on metastasis-risk prediction after endoscopic submucosal dissection (ESD) in colorectal cancer. ## Architecture and inputs Each checkpoint is a PyTorch `state_dict` for `crc_esd.GatedABMILClassifier`: a gated ABMIL slide encoder followed by a 1,536 → 256 → 1 classifier. Inputs are UNI-2h H&E patch embeddings at 20× magnification, generated from 256-pixel non-overlapping patches. An input HDF5 file requires a `features` array with shape `(n_patches, 1536)`; `coords` is additionally required for Grad-CAM visualisation. `best_model_fold3.pth` is the selected checkpoint used in the reported Grad-CAM analyses. The other checkpoints are retained for reproducibility of the patient-level five-fold experiment. ## Usage Install the compatible TRIDENT package and the code package from [zzz-pathology/CRC-ESD](https://github.com/zzz-pathology/CRC-ESD), then run: ```bash pip install -e '.[visualization]' crc-esd-predict --features /path/to/slide_features.h5 --checkpoint best_model_fold3.pth crc-esd-gradcam --features /path/to/slide_features.h5 --checkpoint best_model_fold3.pth --output attention.npz ``` ## Intended use and limitations These parameters are for non-commercial research and reproducibility only. Performance was estimated by internal patient-level five-fold cross-validation in 113 patients (21 metastasis, 92 non-metastasis), with mean AUC 0.918 ± 0.069. The model has not undergone external validation or prospective clinical evaluation and is not a medical device; it must not guide patient management. No whole-slide images, clinical metadata, or patient-level cellular features are released with these parameters.

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