Unsupervised Domain Adaptation for Medical Images with an Improved Combination of Losses

Ravi Gupta, Shounak Das, Amit Sethi

2024

Abstract

This paper presents a novel approach for unsupervised domain adaptation that is tested on H&E stained histology and retinal fundus images. Existing adversarial domain adaptation methods may not effectively align different domains of multimodal distributions associated with classification problems. Since our objective is to enhance domain alignment and reduce domain shifts between these domains by leveraging their unique characteristics, we propose a tailored loss function to address the challenges specific to medical images. This loss combination not only makes the model accurate and robust but also faster in terms of training convergence. We specifically focus on leveraging texture-specific features, such as tissue structure and cell morphology, to enhance adaptation performance in the histology domain. The proposed method – Domain Adaptive Learning (DAL) – was extensively evaluated for accuracy, robustness, and generalization. We conducted experiments on the FHIST and a retina dataset and the results show that DAL significantly surpasses the ViT-based and CNN-based state-of-the-art methods by 1.41% and 6.56% respectively for FHIST dataset while also showing improved results for the retina dataset.

Paper Citation

in Harvard Style

Gupta R., Das S. and Sethi A. (2024). Unsupervised Domain Adaptation for Medical Images with an Improved Combination of Losses. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING; ISBN 978-989-758-688-0, SciTePress, pages 205-215. DOI: 10.5220/0012328100003657

in Bibtex Style

@conference{bioimaging24,
author={Ravi Gupta and Shounak Das and Amit Sethi},
title={Unsupervised Domain Adaptation for Medical Images with an Improved Combination of Losses},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING},
year={2024},
pages={205-215},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012328100003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING
TI - Unsupervised Domain Adaptation for Medical Images with an Improved Combination of Losses
SN - 978-989-758-688-0
AU - Gupta R.
AU - Das S.
AU - Sethi A.
PY - 2024
SP - 205
EP - 215
DO - 10.5220/0012328100003657
PB - SciTePress