Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification

Shota Harada; Ryoma Bise; Kengo Araki; Akihiko Yoshizawa; Kazuhiro Terada; Mariyo Kurata; Naoki Nakajima; Hiroyuki Abe; Tetsuo Ushiku; Seiichi Uchida

arXiv · 2303.01283

Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification

Shota Harada·Ryoma Bise·Kengo Araki·Akihiko Yoshizawa·Kazuhiro Terada·Mariyo Kurata·Naoki Nakajima·Hiroyuki Abe·Tetsuo Ushiku·Seiichi Uchida

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Abstract

Semi-supervised domain adaptation is a technique to build a classifier for a target domain by modifying a classifier in another (source) domain using many unlabeled samples and a small number of labeled samples from the target domain. In this paper, we develop a semi-supervised domain adaptation method, which has robustness to class-imbalanced situations, which are common in medical image classification tasks. For robustness, we propose a weakly-supervised clustering pipeline to obtain high-purity clusters and utilize the clusters in representation learning for domain adaptation. The proposed method showed state-of-the-art performance in the experiment using severely class-imbalanced pathological image patches.

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Shota Harada, Ryoma Bise, Kengo Araki, Akihiko Yoshizawa, Kazuhiro Terada, Mariyo Kurata, Naoki Nakajima, Hiroyuki Abe, Tetsuo Ushiku, Seiichi Uchida. 2023-03-02. Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification. https://arxiv.org/abs/2303.01283

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