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Deep multiple insta...
Deep multiple instance learning versus conventional deep single instance learning for interpretable oral cancer detection
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- Koriakina, Nadezhda, 1991- (författare)
- Uppsala universitet,Bildanalys och människa-datorinteraktion,Centre for Image Analysis, Department of Information Technology, Uppsala University, Uppsala, Sweden
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- Sladoje, Nataša (författare)
- Uppsala universitet,Bildanalys och människa-datorinteraktion,Avdelningen Vi3,Centre for Image Analysis, Department of Information Technology, Uppsala University, Uppsala, Sweden
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- Basic, Vladimir (författare)
- Jönköping University,HHJ, Avdelningen för klinisk diagnostik,HHJ. Studies on Integrated Health and Welfare (SIHW)
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- Lindblad, Joakim (författare)
- Uppsala universitet,Bildanalys och människa-datorinteraktion,Avdelningen Vi3,Centre for Image Analysis, Department of Information Technology, Uppsala University, Uppsala, Sweden
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(creator_code:org_t)
- Public Library of Science (PLoS), 2024
- 2024
- Engelska.
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 19:4 April
- Relaterad länk:
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https://doi.org/10.1...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- The current medical standard for setting an oral cancer (OC) diagnosis is histological examination of a tissue sample taken from the oral cavity. This process is time-consuming and more invasive than an alternative approach of acquiring a brush sample followed by cytological analysis. Using a microscope, skilled cytotechnologists are able to detect changes due to malignancy; however, introducing this approach into clinical routine is associated with challenges such as a lack of resources and experts. To design a trustworthy OC detection system that can assist cytotechnologists, we are interested in deep learning based methods that can reliably detect cancer, given only per-patient labels (thereby minimizing annotation bias), and also provide information regarding which cells are most relevant for the diagnosis (thereby enabling supervision and understanding). In this study, we perform a comparison of two approaches suitable for OC detection and interpretation: (i) conventional single instance learning (SIL) approach and (ii) a modern multiple instance learning (MIL) method. To facilitate systematic evaluation of the considered approaches, we, in addition to a real OC dataset with patient-level ground truth annotations, also introduce a synthetic dataset—PAP-QMNIST. This dataset shares several properties of OC data, such as image size and large and varied number of instances per bag, and may therefore act as a proxy model of a real OC dataset, while, in contrast to OC data, it offers reliable per-instance ground truth, as defined by design. PAP-QMNIST has the additional advantage of being visually interpretable for non-experts, which simplifies analysis of the behavior of methods. For both OC and PAP-QMNIST data, we evaluate performance of the methods utilizing three different neural network architectures. Our study indicates, somewhat surprisingly, that on both synthetic and real data, the performance of the SIL approach is better or equal to the performance of the MIL approach. Visual examination by cytotechnologist indicates that the methods manage to identify cells which deviate from normality, including malignant cells as well as those suspicious for dysplasia. We share the code as open source.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cancer and Oncology (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Annan data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Other Computer and Information Science (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Nyckelord
- Deep Learning
- Humans
- Mouth Neoplasms
- Neural Networks
- Computer
- Article
- artificial neural network
- cancer cell
- cancer diagnosis
- conventional deep single instance learning
- cytotechnologist
- deep multiple instance learning
- human
- lenet
- machine learning
- mouth cancer
- resnet18
- squeezenet
- comparative study
- mouth tumor
- pathology
- Computerized Image Processing
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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