Conditional Deep Convolutional Neural Networks for Improving the Automated Screening of Histopathological Images

TL;DR

This paper introduces a conditional deep learning model for histopathological image segmentation that adapts to data variability and improves accuracy over traditional methods by using a novel conditioning strategy.

Contribution

It presents a novel conditional Fully Convolutional Network architecture and an automated data selection method for improved histopathology image segmentation.

Findings

The conditioned co-FCN outperforms U-Net on challenging metastasis detection tasks.

Automated conditioning data selection enhances model generalization across centers.

The approach effectively handles domain shift in histopathological image analysis.

Abstract

Semantic segmentation of breast cancer metastases in histopathological slides is a challenging task. In fact, significant variation in data characteristics of histopathology images (domain shift) make generalization of deep learning to unseen data difficult. Our goal is to address this challenge by using a conditional Fully Convolutional Network (co-FCN) whose output can be conditioned at run time, and which can improve its performance when a properly selected set of reference slides are used to condition the output. We adapted to our task a co-FCN originally applied to organs segmentation in volumetric medical images and we trained it on the Whole Slide Images (WSIs) from three out of five medical centers present in the CAMELYON17 dataset. We tested the performance of the network on the WSIs of the remaining centers. We also developed an automated selection strategy for selecting the conditioning subset, based on an unsupervised clustering process applied to a target-specific set of reference patches, followed by a selection policy that relies on the cluster similarities with the input patch. We benchmarked our proposed method against a U-Net trained on the same dataset with no conditioning. The conditioned network shows better performance that the U-Net on the WSIs with Isolated Tumor Cells and micro-metastases from the medical centers used as test. Our contributions are an architecture which can be applied to the histopathology domain and an automated procedure for the selection of conditioning data.