CRC-ESD: Metastasis Risk Prediction Model
Trained model weights for "Deep Learning-Based Analysis of H&E-Stained Histopathological Images for Predicting Metastasis Risk After Endoscopic Submucosal Dissection in Colorectal Cancer".
Model Description
- Architecture: FeatherSlideEncoder (gated attention ABMIL) + 2-layer MLP classifier
- Input: UNI-2h patch features (1536-d → 512-d embedding) from H&E WSIs
- Output: Binary metastasis risk probability
- Training: 5-fold stratified cross-validation on 113 CRC patients (294 WSIs)
- Performance: Mean AUC = 0.759 ± 0.041
Files
| File | Description |
|---|---|
best_model_fold0.pth |
Fold 0 best checkpoint |
best_model_fold1.pth |
Fold 1 best checkpoint |
best_model_fold2.pth |
Fold 2 best checkpoint |
best_model_fold3.pth |
Fold 3 best checkpoint |
best_model_fold4.pth |
Fold 4 best checkpoint |
Usage
import torch
from trident.slide_encoder_models import FeatherSlideEncoder
# Load model
model = FeatherSlideEncoder()
classifier = torch.nn.Sequential(
torch.nn.Linear(512, 256),
torch.nn.ReLU(),
torch.nn.Dropout(0.5),
torch.nn.Linear(256, 1)
)
checkpoint = torch.load("best_model_fold0.pth", map_location="cpu")
model.load_state_dict(checkpoint["encoder_state_dict"])
classifier.load_state_dict(checkpoint["classifier_state_dict"])
Citation
@article{zhao2025crc_esd,
title={Deep Learning-Based Analysis of H&E-Stained Histopathological Images for Predicting Metastasis Risk After Endoscopic Submucosal Dissection in Colorectal Cancer},
author={Zhao, Zhixun and Gong, Changhao and Shi, Yihang and others},
journal={npj Digital Medicine},
year={2025}
}