Since the 1980s, deep learning and biomedical engineering have been coevolving and feeding each other. The breadth, complexity, and rapidly expanding size of biomedical data have stimulated the development of novel deep learning methods, and application of these methods to biomedical data have led to scientific discoveries and practical solutions. Nowadays, neural network (NN), convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM) networks have been widely applied in various biomedical applications, such as disease detection and prevention, cancer detection and prevention, disease prediction, experimental medicine, emergency, medication management, healthcare management. Particularly, due to the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge, deep learning is rapidly becoming the state of the art, leading to enhanced performance in the biomedical image analysis domain, such as image segmentation, image registration, image fusion, image annotation, computer-aided diagnosis (CADx) and prognosis, lesion/landmark detection (34–36), and microscopic image analysis. It is essential to acknowledge a strong two-way relationship between deep learning and bioinformatics and biomedicine. In one direction, deep learning helps the bioinformatics and biomedicine, both broadly, by providing powerful methods for analyzing biomedical data, and more narrowly, by providing simplified but useful computational models for neuroscience. In the other direction, it is our knowledge of the human brain that has provided the fundamental source of inspiration for AI and deep learning, while all areas of bioinformatics and biomedicine have provided challenging problems that have inspired researchers to push the boundaries of deep learning methods.

Deep learning techniques have achieved state-of-the-art performance across different biomedical applications; however, there is still room for improvement. First, as witnessed in computer vision, in which breakthrough improvements were achieved by use of large numbers of training data, a large, publicly available data set of medical images from which deep models can find more generalized features would lead to improved performance. Second, although data-driven feature representations, especially in an unsupervised manner, have helped enhance accuracy, it would be desirable to devise a new methodological architecture involving domain-specific knowledge. Third, it is necessary to develop algorithmic techniques to efficiently handle biomedical data acquired with different scanning protocols so that it would not be necessary to train modality-specific deep models. Finally, when using deep learning to investigate underlying patterns, because of the black box–like characteristics of deep models, it remains challenging to understand and interpret the learned models intuitively. To face these challenges, this workshop named “Deep Learning Techniques for Bioinformatics and Biomedicine” in conjunction with the 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2024) will solicit papers on various deep learning techniques for bioinformatics and biomedicine.

The direct link for paper submission is https://wi-lab.com/cyberchair/2024/bibm24/scripts/submit.php?subarea=S13&undisplay_detail=1&wh=/cyberchair/2024/bibm24/scripts/ws_submit.php

• Deep learning for disease detection and prevention

• Deep learning for cancer detection and prevention

• Deep learning for disease prediction

• Deep learning for experimental medicine

• Deep learning for emergency

• Deep learning for medication management

• Deep learning for healthcare management

• Deep learning for human motion analysis

• Bioinformatics and biomedicine applications based on deep learning

• Bioinformatics and biomedicine applications based on reinforcement learning

• Bioinformatics and biomedicine applications based on federated learning

• Bioinformatics and biomedicine applications based on machine learning

• Due date for full workshop papers submission: Oct. 10, 2024

• Notification of paper acceptance to authors: Nov. 5, 2024

• Camera-ready of accepted papers: Nov. 21, 2024

• Workshop: Dec. 5-8, 2024

• Prof. Fuquan Zhang (Minjiang University, China)

• Prof. Xingsi Xue (Fujian University of Technology, China)

• Prof. Tianyu Huang (Beijing Institute of Technology, China)

• Prof. Jianhui Lv (Tsinghua University, China)

• Prof. Qing Lv (Taiyuan University of Technology, China)

• Prof. Feng-Jang Hwang (University of Technology Sydney, Australia)

• Prof. Hsiao-Ting Tseng (National Central University, Taiwan)

• Dr. Chuansheng Wang (Universitat Politècnica de Catalunya)

• Dr. Yi Mei (Victoria University of Wellington, New Zealand)

• Prof. Jie Sui, IEEE Senior Member (University of Aberdeen, United Kingdom)

• Prof. Jianhua Liu (Fujian University of Technology, China)

• Prof. Yuemin Ding (University of Navarra, Spain)

• Dr. Pei-Wei Tsai (Swinburne University of Technology, Australia)

• Prof. Chunjia Han (University of Greenwich, United Kingdom)

• Prof. Ting Bi (Maynooth University, Ireland)

• Dr. Cai Dai (Shaanxi Normal University, China)

• Dr. Junfeng Chen (Hohai University, China)

• Prof. Hai Zhu (Zhoukou Normal University, China)

• Prof. Miao Ye (Guilin University of Electronic Technology, China)

• Prof. Chin-Ling Chen (Chaoyang University of Technology, Taiwan)

• Prof. Yikun Huang (Concord University College Fujian Normal University, China)

• Dr. Linjuan Ma (Beijing Institute of Technology, China)

• Dr. Xueping Peng (University of Technology Sydney, Australia)

• Dr. James Chambua (University of Dar-es-Salaam, Tanzania)

• Dr. Yifan Zhu, IEEE Member (Tsinghua University, China)

• Dr. Yu Mao (Minnan Normal University, China)