On the morning of October 6, local time, the 3rd Workshop on Computational Mathematics Modeling in Cancer Analysis (CMMCA2024) was successfully held at the MICCAI2024 conference in Marrakech, Morocco. The symposium aimed to advance scientific research in the field of computational mathematics for cancer analysis, focusing on the development trends and challenges in the mathematical theory, computation, and applications of cancer data analysis, and bringing new insights to cancer research and clinical practice.
At the symposium, Dr. Lequan Yu from the University of Hong Kong delivered a keynote speech titled "Integrating Deep Learning in Computational Pathology: From Unimodal to Multimodal". Dr. Yu delved into the application of multimodal methods in computational pathology and shared how to provide new perspectives and possibilities for disease diagnosis and treatment by integrating different types of data. He emphasized that this multimodal integration can not only enhance the performance of models but also provide a more comprehensive understanding of complex biomedical phenomena, promoting the development of precision medicine. Through specific cases and the latest research results, Dr. Yu demonstrated the potential of multimodal learning in practical applications, inspiring discussions and reflections among the participants on future research directions.
Dr. Pingkun Yan from Rensselaer Polytechnic Institute presented a keynote speech titled "Foundation Models for Cancer Image Analysis". In this presentation, Dr. Yan explored the application of foundation models in cancer image analysis in depth and introduced in detail how they can effectively process and analyze complex medical image data. He demonstrated the advantages of foundation models in cancer detection and classification tasks, highlighting their great potential in improving diagnostic accuracy and efficiency.
Dr. Nasir Rajpoot from the University of Warwick gave a presentation titled "Computational Pathology Completes the Puzzle of Precision Cancer Therapy". He explored the importance of computational pathology in precision cancer therapy, emphasizing the customization of treatment plans for patients. Dr. Rajpoot introduced how to analyze pathological images by combining artificial intelligence and machine learning techniques to improve the accuracy of tumor diagnosis.
Dr. Caroline Chung from MD Anderson Cancer Center delivered a keynote speech titled "Leveraging Quantitative Imaging Techniques for Digital Twins in Oncology". In this session, Dr. Chung explored the application potential of digital twin technology in the field of oncology in detail and introduced the concept of digital twins, which is to create a virtual model corresponding to a patient's entity through a computational model to better understand and predict disease progression. Dr. Chung emphasized that by combining quantitative imaging techniques, detailed biological information can be obtained, enabling precise analysis of tumor characteristics.
Chaired by Dr. Chao Li from the University of Cambridge, the conference held a special session focusing on the cutting - edge content of cancer computation. The participating experts had in - depth discussions on how to use computational technology to enhance cancer research and treatment. During the discussion, the participants shared application examples of the latest computational models and algorithms in tumor prediction, diagnosis, and treatment. The participants also discussed the potential of machine learning and artificial intelligence in oncology and explored how to improve the accuracy and interpretability of models for better application in clinical practice.
The conference also had an academic paper discussion session, where 12 selected papers presented the latest research results through posters and oral presentations.
This symposium promoted international academic exchanges and cooperation, delved into the cutting - edge applications of artificial intelligence in cancer computation, and advanced the development of AI in the field of cancer computation.
Chinese version: https://mp.weixin.qq.com/s/1zScNCA2QHZAWWVE5D6yAw
