BIBE-PICA' 24: Pathology Image Computing and Analysis

Pathologic tissue biopsy is the "gold standard" for clinical cancer diagnosis. With the development of artificial intelligence, computational pathology, which integrates deep learning and other technologies, has greatly improved the efficiency and accuracy of cancer diagnosis, treatment and prognosis analysis. Computational pathology analyzes disease images through deep learning technology to reveal the subtle development process of diseases and promote the development of personalized medicine, especially in the treatment of complex diseases such as tumors. By analyzing the molecular and genetic characteristics of tumors, computational pathology can recommend the most appropriate treatment for each patient. In addition, the research results in this field have greatly facilitated the development of clinical research, not only to evaluate the treatment effect and monitor the disease progress in clinical trials, but also to conduct quantitative analysis of drug efficacy and accelerate the development and approval process of new drugs. The 7th International Conference on Bioinformatics and Biomedical Engineering (BIBE) aims to promote academic exchanges and cooperation in the field of bioinformatics and biomedical engineering, therefore, a special session on "Pathology Image Computing and Analysis" has been set up to focus on the cutting-edge technologies, research results, and future development trends in this field.

BIBE-PICA' 24 topics include but are not limited to

1. virtual staining of pathology images
2. Semantic segmentation of pathological tissue
3. Cancer subtype classification and staging
4. Survivability analysis based on pathological images
5. Pathology image-based drug efficacy prediction
6. biomarker prediction based on pathology images
7. whole-slice pathology image analysis based on multi-sample learning

BIBE-PICA' 24 organizer/session chair

Yifeng Wang
Ph.D. of Harbin Institute of Technology-National University of Singapore joint training (Scholarship Council of the State Scholarship Council of Singapore), member of China Computer Federation, member of Asia-Pacific Neural Networks Association, member of MICCAI, and member of AAAI. His main research interests include deep learning model design and interpretability analysis, generative deep learning models, virtual staining of pathological images, tissue semantic Segmentation, cancer diagnosis and prediction based on deep learning. He is the chairperson of the Guangdong Science and Technology Innovation Strategy Project, and has participated in the key projects of the National Natural Science Foundation of China, the 14th Five-Year Plan of the State Key Research and Development Program, and the Guangdong Province Key Areas Special Project.