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

06/29/2022
by   Yash Sharma, et al.
0

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 ).

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/05/2022

LE-UDA: Label-efficient unsupervised domain adaptation for medical image segmentation

While deep learning methods hitherto have achieved considerable success ...
research
04/20/2022

Unsupervised Domain Adaptation for Cardiac Segmentation: Towards Structure Mutual Information Maximization

Unsupervised domain adaptation approaches have recently succeeded in var...
research
10/23/2021

Domain Adaptation via Maximizing Surrogate Mutual Information

Unsupervised domain adaptation (UDA), which is an important topic in tra...
research
10/05/2022

WUDA: Unsupervised Domain Adaptation Based on Weak Source Domain Labels

Unsupervised domain adaptation (UDA) for semantic segmentation addresses...
research
01/09/2022

Preserving Domain Private Representation via Mutual Information Maximization

Recent advances in unsupervised domain adaptation have shown that mitiga...
research
08/19/2022

Cross-Domain Evaluation of a Deep Learning-Based Type Inference System

Optional type annotations allow for enriching dynamic programming langua...
research
07/08/2019

Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching

A broad range of cross-m-domain generation researches boil down to match...

Please sign up or login with your details

Forgot password? Click here to reset