MaNi: Maximizing Mutual Information for Nuclei Cross-Domain Unsupervised Segmentation

TL;DR

This paper introduces MaNi, a lightweight mutual information maximization approach for unsupervised domain adaptation in nuclei segmentation, effectively transferring knowledge across diverse cancer types with minimal additional components.

Contribution

MaNi is a novel MI-based method that enhances cross-domain nuclei segmentation without complex modules, applicable across various models and datasets.

Findings

Achieves competitive performance across 20+ cancer domain shifts.

Requires only one negative pair per positive pair for MI maximization.

Easily integrable into existing segmentation frameworks.

Abstract

In this work, we propose a mutual information (MI) based unsupervised domain adaptation (UDA) method for the cross-domain nuclei segmentation. Nuclei vary substantially in structure and appearances across different cancer types, leading to a drop in performance of deep learning models when trained on one cancer type and tested on another. This domain shift becomes even more critical as accurate segmentation and quantification of nuclei is an essential histopathology task for the diagnosis/ prognosis of patients and annotating nuclei at the pixel level for new cancer types demands extensive effort by medical experts. To address this problem, we maximize the MI between labeled source cancer type data and unlabeled target cancer type data for transferring nuclei segmentation knowledge across domains. We use the Jensen-Shanon divergence bound, requiring only one negative pair per positive pair for MI maximization. We evaluate our set-up for multiple modeling frameworks and on different datasets comprising of over 20 cancer-type domain shifts and demonstrate competitive performance. All the recently proposed approaches consist of multiple components for improving the domain adaptation, whereas our proposed module is light and can be easily incorporated into other methods (Implementation: https://github.com/YashSharma/MaNi ).