@inproceedings{fb16a551a3db45668987c4192a684e6a,
title = "Interaction-Oriented Feature Decomposition for Medical Image Lesion Detection",
abstract = "Common lesion detection networks typically use lesion features for classification and localization. However, many lesions are classified only by lesion features without considering the relation with global context features, which raises the misclassification problem. In this paper, we propose an Interaction-Oriented Feature Decomposition (IOFD) network to improve the detection performance on context-dependent lesions. Specifically, we decompose features output from a backbone into global context features and lesion features that are optimized independently. Then, we design two novel modules to improve the lesion classification accuracy. A Global Context Embedding (GCE) module is designed to extract global context features. A Global Context Cross Attention (GCCA) module without additional parameters is designed to model the interaction between global context features and lesion features. Besides, considering the different features required by classification and localization tasks, we further adopt a task decoupling strategy. IOFD is easy to train and end-to-end in terms of training and inference. The experimental results for datasets in two modalities outperform state-of-the-art algorithms, which demonstrates the effectiveness and generality of IOFD. The source code is available at https://github.com/mklz-sjy/IOFD",
keywords = "Context embedding, Cross attention, Lesion detection, Medical image",
author = "Junyong Shen and Yan Hu and Xiaoqing Zhang and Zhongxi Qiu and Tingming Deng and Yanwu Xu and Jiang Liu",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 ; Conference date: 18-09-2022 Through 22-09-2022",
year = "2022",
doi = "10.1007/978-3-031-16437-8_31",
language = "English",
isbn = "9783031164361",
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 = "324--333",
editor = "Linwei Wang and Qi Dou and Fletcher, {P. Thomas} and Stefanie Speidel and Shuo Li",
booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings",
address = "Germany",
}