Prostate Cancer
Prostate cancer research intensely focuses on improving diagnosis and treatment through advanced imaging and AI-driven analysis. Current efforts leverage deep learning models, including U-Nets, transformers, and various convolutional neural networks, to analyze multi-parametric MRI, ultrasound, and digital pathology images for accurate segmentation, grading (Gleason score), and risk stratification. These advancements aim to enhance diagnostic accuracy, personalize treatment plans, and ultimately improve patient outcomes by facilitating earlier detection and more effective management of this prevalent cancer. The development of robust, reproducible AI tools that generalize across different imaging platforms and datasets is a key challenge and focus.
Papers
Deep Learning-based Unsupervised Domain Adaptation via a Unified Model for Prostate Lesion Detection Using Multisite Bi-parametric MRI Datasets
Hao Li, Han Liu, Heinrich von Busch, Robert Grimm, Henkjan Huisman, Angela Tong, David Winkel, Tobias Penzkofer, Ivan Shabunin, Moon Hyung Choi, Qingsong Yang, Dieter Szolar, Steven Shea, Fergus Coakley, Mukesh Harisinghani, Ipek Oguz, Dorin Comaniciu, Ali Kamen, Bin Lou
Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic review
Anshu Ankolekar, Sebastian Boie, Maryam Abdollahyan, Emanuela Gadaleta, Seyed Alireza Hasheminasab, Guang Yang, Charles Beauville, Nikolaos Dikaios, George Anthony Kastis, Michael Bussmann, Sara Khalid, Hagen Kruger, Philippe Lambin, Giorgos Papanastasiou