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SMDetector :
SMDetector : Small mitotic detector in histopathology images using faster R-CNN with dilated convolutions in backbone model
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- Khan, Hameed Ullah (author)
- Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan
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- Raza, Basit (author)
- Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan
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- Shah, Munawar Hussain (author)
- Pathology Department, Nishtar Medical University, Multan, Pakistan
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- Usama, Syed Muhammad (author)
- Post Graduate Resident Surgeon at College of Physicians and Surgeons Pakistan (CPSP), Karachi, Pakistan
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- Tiwari, Prayag, 1991- (author)
- Högskolan i Halmstad,Akademin för informationsteknologi
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- Band, Shahab S. (author)
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Yunlin, Douliou, Taiwan
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(creator_code:org_t)
- Amsterdam : Elsevier, 2023
- 2023
- English.
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In: Biomedical Signal Processing and Control. - Amsterdam : Elsevier. - 1746-8094 .- 1746-8108. ; 81
- Related links:
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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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Abstract
Subject headings
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- Breast cancer is one of the most common cancer types among women, and it is a deadly disease caused by the uncontrolled proliferation of cells. Pathologists face a challenging issue of mitotic cell identification and counting during manual detection and identification of cancer. Artificial intelligence can help the medical professional with early, quick, and accurate diagnosis of breast cancer. Consequently, the survival rate will be improved and mortality rate will be decreased. Different deep learning techniques are used in computational pathology for cancer diagnosis. In this study, the SMDetector is proposed which is a deep learning model for detecting small objects such as mitotic and non-mitotic nuclei. This model employs dilated layers in the backbone to prevent small objects from disappearing in the deep layers. The purpose of the dilated layers in this model is to reduce the size gap between the image and the objects it contains. Region proposal network is optimized to accurately identify small objects. The proposed model yielded overall average precision (AP) of 50.31% and average recall (AR) of 55.90% that outperforms the existing standard object detection models on ICPR 2014 (Mitos-Atypia-14) dataset. To best of our knowledge the proposed model is state-of-the-art model for precision and recall of mitotic object detection on ICPR 2014 (Mitos-Atypia-14) dataset. The proposed model has achieved average precision for mitotic nuclei 68.49%, average recall for mitotic nuclei 59.86% and f-measure 63.88%. © 2022 The Authors
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Hälsovetenskap -- Annan hälsovetenskap (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Health Sciences -- Other Health Sciences (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Robotteknik och automation (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Robotics (hsv//eng)
Keyword
- Convolutional neural network
- Faster R-CNN
- Breast cancer
- Computational pathology
- Mitotic nuclei detection
- Hälsoinnovation
- Health Innovation
Publication and Content Type
- ref (subject category)
- art (subject category)
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