_:n6004393b30b04ac5946ecd0c32b7cc5bxb46
<http://schema.org/description>
"Prostate cancer is the most prevalent cancer among men in Western countries,\nwith 1.1 million new diagnoses every year. The gold standard for the diagnosis\nof prostate cancer is a pathologists' evaluation of prostate tissue.\n  To potentially assist pathologists deep-learning-based cancer detection\nsystems have been developed. Many of the state-of-the-art models are\npatch-based convolutional neural networks, as the use of entire scanned slides\nis hampered by memory limitations on accelerator cards. Patch-based systems\ntypically require detailed, pixel-level annotations for effective training.\nHowever, such annotations are seldom readily available, in contrast to the\nclinical reports of pathologists, which contain slide-level labels. As such,\ndeveloping algorithms which do not require manual pixel-wise annotations, but\ncan learn using only the clinical report would be a significant advancement for\nthe field.\n  In this paper, we propose to use a streaming implementation of convolutional\nlayers, to train a modern CNN (ResNet-34) with 21 million parameters end-to-end\non 4712 prostate biopsies. The method enables the use of entire biopsy images\nat high-resolution directly by reducing the GPU memory requirements by 2.4 TB.\nWe show that modern CNNs, trained using our streaming approach, can extract\nmeaningful features from high-resolution images without additional heuristics,\nreaching similar performance as state-of-the-art patch-based and\nmultiple-instance learning methods. By circumventing the need for manual\nannotations, this approach can function as a blueprint for other tasks in\nhistopathological diagnosis.\n  The source code to reproduce the streaming models is available at\nhttps://github.com/DIAGNijmegen/pathology-streaming-pipeline ."
<https://deepai.org/profile/wouter-bulten>