# Weakly supervised training of pixel resolution segmentation models on whole slide images

## TL;DR

This paper introduces a weakly supervised method for training pixel-level segmentation models on whole slide images, using confidence-based patch sampling and a noise-robust divergence to handle noisy labels, demonstrated on cancer data.

## Contribution

It proposes a novel training approach combining confidence-guided patch sampling and a noise-robust KL divergence extension for weakly supervised segmentation.

## Key findings

- Successful tumor segmentation with morphological consistency
- Effective handling of noisy labels in weak supervision
- Promising results on CAMELYON 16 dataset

## Abstract

We present a novel approach to train pixel resolution segmentation models on whole slide images in a weakly supervised setup. The model is trained to classify patches extracted from slides. This leads the training to be made under noisy labeled data. We solve the problem with two complementary strategies. First, the patches are sampled online using the model's knowledge by focusing on regions where the model's confidence is higher. Second, we propose an extension of the KL divergence that is robust to noisy labels. Our preliminary experiment on CAMELYON 16 data set show promising results. The model can successfully segment tumor areas with strong morphological consistency.