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Paper Detail

Paper IDMLR-APPL-BSIP.9
Paper Title ADVANCED DEEP NETWORK WITH ATTENTION AND GENETIC-DRIVEN REINFORCEMENT LEARNING LAYER FOR AN EFFICIENT CANCER TREATMENT OUTCOME PREDICTION
Authors Francesco Rundo, STMicroelectronics, Italy; Giuseppe Luigi Banna, Queen Alexandra Hospital, United Kingdom; Francesca Trenta, Sebastiano Battiato, University of Catania, Italy
SessionMLR-APPL-BSIP: Machine learning for biomedical signal and image processing
LocationArea C
Session Time:Wednesday, 22 September, 08:00 - 09:30
Presentation Time:Wednesday, 22 September, 08:00 - 09:30
Presentation Poster
Topic Applications of Machine Learning: Machine learning for biomedical signal and image processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract In the last few years, medical researchers have investigated promising approaches for cancer treatment, leading to a major interest in the immunotherapeutic approach. The target of immunotherapy is to boost a subject's immune system in order to fight cancer. However, scientific studies confirmed that not all patients have a positive response to immunotherapy treatment. Medical research has long been engaged in the search for predictive immunotherapeutic-response bio-markers. Based on these considerations, we developed a non-invasive advanced pipeline with a downstream 3D deep classifier with attention and reinforcement learning for early prediction of patients responsive to immunotherapeutic treatment from related chest-abdomen CT-scan imaging. We have tested the proposed pipeline within a clinical trial that recruited patients with metastatic bladder cancer. Our experiment results achieved accuracy close to 93\%.