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CRC ESD

This repository provides the five patient-level cross-validation checkpoints for research on metastasis-risk prediction after endoscopic submucosal dissection (ESD) in colorectal cancer.

KindOtherVersionBest model fold0Cgrade

Model source

Kind
Other
File size
25.5 MB
Version
vBest model fold0
Updated
Oct 22, 2026
Source
Hugging Face

Source description

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, then run:

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.

Sources

1 source
Hugging FacePrimaryVersion ebb44fbc6311f5789379ded18f5c0ebcb82ed7fc
huggingface.co/ybcy/CRC-ESD/tree/ebb44fbc6311f5789379ded18f5c0ebcb82ed7fc

Verified Oct 22

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Model artifacts

5 artifacts

best_model_fold0.pth

pth · 25.5 MB · SHA-256 38d0ee501a7e…86b8 · Hugging Face

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best_model_fold1.pth

pth · 25.5 MB · SHA-256 dfe25a448a98…85dc · Hugging Face

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best_model_fold2.pth

pth · 25.5 MB · SHA-256 8fab67b24509…cbf3 · Hugging Face

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best_model_fold3.pth

pth · 25.5 MB · SHA-256 4cac30839c97…5a96 · Hugging Face

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best_model_fold4.pth

pth · 25.5 MB · SHA-256 75f49687530f…9686 · Hugging Face

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Source excerpts

2 excerpts

--- 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.

Source context: 0 downloads · 0 likes · Pipeline image-classification · Repo ybcy/CRC-ESD