CRC_LNM_Pred

Introduction

H&E whole slide images to predict metastasis of lymph node in T1 colorectal cancer using endoscopically resected specimens.

Notice: This repository is under construction, the full repository will be completed soon.

Requirement

• python >= 3.6
  • numpy >=1.17.4
  • openslide-python >= 1.1.2
  • pandas >= 1.1.3
  • scikit-image >= 0.15.0
  • scikit-learn >= 0.23.2
  • torch >= 1.5.1 (https://pytorch.org/)
  • torchvision >= 0.6.1
• openslide >= 3.4.1 (https://openslide.org/)

Usage

• python make_image_list_dict.py
  • prepare training and test data
• python train_patch_image.py
  • train patch-level image feature extractor
• python test_patch_image.py
  • test patch-level image LNM prediction
• python train_slide.py
  • train slide-level end-to-end LNM prediction model
• python test_slide.py
  • test slide-level LNM prediction
• python show_attention_map.py
  • show attention map of the predicted slide

Reference

"Utility of artificial intelligence with deep learning of hematoxylin and eosin-stained whole slide images to predict metastasis of lymph node in T1 colorectal cancer using endoscopically resected specimens" -> paper is under review