2017/11/28 by Simon Köhl, Kohl, Simon, David Bonekamp +13
Computer Science · #AI in cancer detection #Advanced Image Processing Techniques #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Neural and Evolutionary Computing (cs.NE)
paper · pdf · doi:10.48550/arxiv.1711.10400
openalex publication_date 2017/11/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The large number of trainable parameters of deep neural networks renders them inherently data hungry. This characteristic heavily challenges the medical imaging community and to make things even worse, many imaging modalities are ambiguous in nature leading to rater-dependant annotations that current loss formulations fail to capture. We propose employing adversarial training for segmentation networks in order to alleviate aforementioned problems. We learn to segment aggressive prostate cancer utilizing challenging MRI images of 152 patients and show that the proposed scheme is superior over the de facto standard in terms of the detection sensitivity and the dice-score for aggressive prostate cancer. The achieved relative gains are shown to be particularly pronounced in the small dataset limit.