@inproceedings{f1e7c8a7e5134ab3869a062aad48fa1c,
title = "Clinical Pixel Feature Recalibration Module for Ophthalmic Image Classification",
abstract = "Ophthalmic image examination has become a commonly-acknowledged way for ocular disease screening and diagnosis. Clinical features extracted from ophthalmic images play different roles in affecting clinicians making diagnosis results, but how to incorporate these clinical features into convolutional neural network (CNN) representations has been less studied. In this paper, we propose a simple yet practical module, Clinical Pixel Feature Recalibration Module (CPF), aiming to exploit the potential of clinical features to improve the ocular disease recognition performance of CNNs. CPF first extracts clinical pixel features from each spatial position of all feature maps by clinical cross-channel pooling, then estimates each spatial position recalibration weight in a pixel-independent clinical fusion. By infusing the relative importance of clinical features into feature maps at the pixel level, CPF is supposed to enhance the representational ability of CNNs. Our CPF is easily inserted into existing CNNs with negligible overhead. We conduct comprehensive experiments on two publicly available ophthalmic image datasets and CIFAR datasets, and the results show the superiority and generation ability of CPF over advanced attention methods. Furthermore, this paper presents an in-depth weight visualization analysis to investigate the inherent behavior of CPF, aiming to improve the interpretability of CNNs in the decision-making process.",
keywords = "CPF, Ophthalmic image, attention, classification, interpretability",
author = "Zhao, {Ji Lu} and Xiaoqing Zhang and Xiao Wu and Zhang, {Zhi Xuan} and Tong Zhang and Heng Li and Yan Hu and Jiang Liu",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 32nd International Conference on Artificial Neural Networks, ICANN 2023 ; Conference date: 26-09-2023 Through 29-09-2023",
year = "2023",
doi = "10.1007/978-3-031-44216-2_8",
language = "English",
isbn = "9783031442155",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "87--98",
editor = "Lazaros Iliadis and Antonios Papaleonidas and Plamen Angelov and Chrisina Jayne",
booktitle = "Artificial Neural Networks and Machine Learning – ICANN 2023 - 32nd International Conference on Artificial Neural Networks, Proceedings",
address = "Germany",
}