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Detection of Prosta...
Detection of Prostate Cancer in Whole-Slide Images Through End-to-End Training With Image-Level Labels
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- Pinckaers, Hans (författare)
- Radboud Univ Nijmegen, Netherlands
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- Bulten, Wouter (författare)
- Radboud Univ Nijmegen, Netherlands
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- van der Laak, Jeroen (författare)
- Linköpings universitet,Avdelningen för diagnostik och specialistmedicin,Medicinska fakulteten,Region Östergötland, Klinisk patologi,Radboud Univ Nijmegen, Netherlands
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- Litjens, Geert (författare)
- Radboud Univ Nijmegen, Netherlands
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(creator_code:org_t)
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021
- 2021
- Engelska.
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Ingår i: IEEE Transactions on Medical Imaging. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 0278-0062 .- 1558-254X. ; 40:7, s. 1817-1826
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Prostate cancer is the most prevalent cancer among men in Western countries, with 1.1 million new diagnoses every year. The gold standard for the diagnosis of prostate cancer is a pathologists evaluation of prostate tissue. To potentially assist pathologists deep/learning/based cancer detection systems have been developed. Many of the state-of-the- art models are patch/based convolutional neural networks, as the use of entire scanned slides is hampered by memory limitations on accelerator cards. Patch-based systems typically require detailed, pixel-level annotations for effective training. However, such annotations are seldom readily available, in contrast to the clinical reports of pathologists, which contain slide-level labels. As such, developing algorithms which do not require manual pixel-wise annotations, but can learn using only the clinical report would be a significant advancement for the field. In this paper, we propose to use a streaming implementation of convolutional layers, to train a modern CNN (ResNet/34) with 21 million parameters end-to-end on 4712 prostate biopsies. Themethod enables the use of entire biopsy images at high-resolution directly by reducing the GPUmemory requirements by 2.4 TB. We show thatmodern CNNs, trained using our streaming approach, can extract meaningful features from high-resolution images without additional heuristics, reaching similar performance as state-of-the-art patch-based and multiple-instance learning methods. By circumventing the need for manual annotations, this approach can function as a blueprint for other tasks in histopathological diagnosis. The source code to reproduce the streaming models is available at https://github.com/DIAGNijmegen/ pathology-streaming-pipeline.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
Nyckelord
- Deep learning; deep convolutional neural networks; computational pathology; prostate cancer
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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