Domain Adaptive Multiple Instance Learning for Instance-level Prediction of Pathological Images

• URL: http://arxiv.org/abs/2304.03537v1
• Date: Fri, 7 Apr 2023 08:31:06 GMT
• Title: Domain Adaptive Multiple Instance Learning for Instance-level Prediction of Pathological Images
• Authors: Shusuke Takahama, Yusuke Kurose, Yusuke Mukuta, Hiroyuki Abe, Akihiko Yoshizawa, Tetsuo Ushiku, Masashi Fukayama, Masanobu Kitagawa, Masaru Kitsuregawa, Tatsuya Harada
• Abstract summary: We propose a new task setting to improve the classification performance of the target dataset without increasing annotation costs. In order to combine the supervisory information of both methods effectively, we propose a method to create pseudo-labels with high confidence.
• Score: 45.132775668689604
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pathological image analysis is an important process for detecting abnormalities such as cancer from cell images. However, since the image size is generally very large, the cost of providing detailed annotations is high, which makes it difficult to apply machine learning techniques. One way to improve the performance of identifying abnormalities while keeping the annotation cost low is to use only labels for each slide, or to use information from another dataset that has already been labeled. However, such weak supervisory information often does not provide sufficient performance. In this paper, we proposed a new task setting to improve the classification performance of the target dataset without increasing annotation costs. And to solve this problem, we propose a pipeline that uses multiple instance learning (MIL) and domain adaptation (DA) methods. Furthermore, in order to combine the supervisory information of both methods effectively, we propose a method to create pseudo-labels with high confidence. We conducted experiments on the pathological image dataset we created for this study and showed that the proposed method significantly improves the classification performance compared to existing methods.

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