Benchmarking Image Transformers for Prostate Cancer Detection from Ultrasound Data

arXiv:2403.18233v1 Announce Type: cross
Abstract: PURPOSE: Deep learning methods for classifying prostate cancer (PCa) in ultrasound images typically employ convolutional networks (CNNs) to detect cancer in small regions of interest (ROI) along a needle trace region. However, this approach suffers from weak labelling, since the ground-truth histopathology labels do not describe the properties of individual ROIs. Recently, multi-scale approaches have sought to mitigate this issue by combining the context awareness of transformers with a CNN feature extractor to detect cancer …

abstract arxiv benchmarking cancer cancer detection cnns cs.cv cs.lg data deep learning detection eess.iv ground-truth however image images labelling labels networks q-bio.to roi small transformers truth type