Papers
1
Total Citations
15
H-Index
1
About
Justin Visak is a rising researcher at the intersection of artificial intelligence and radiation oncology, whose work is shaping the future of precision cancer treatment. His primary focus lies in developing deep learning models to optimize radiotherapy planning, with a particular emphasis on multimodal dose prediction. In his most cited work, "Multimodal radiotherapy dose prediction using a multi‐task deep learning model" (2024, 15 citations), Visak tackles the challenge of accelerating partial breast irradiation (APBI), a targeted treatment modality that offers shorter courses and more precise dose delivery than conventional whole breast irradiation. By designing a multi-task framework that integrates diverse imaging and treatment data, his model improves the accuracy and efficiency of dose prediction across different APBI modalities. This contribution is notable for its potential to streamline clinical workflows and enhance patient outcomes. Visak’s work demonstrates a clear commitment to bridging computational innovation with practical medical applications, making him a promising voice in the growing field of AI-assisted radiotherapy.
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