Version 1
: Received: 6 May 2024 / Approved: 8 May 2024 / Online: 8 May 2024 (10:34:37 CEST)
How to cite:
Kondejkar, T.; Al-Heejawi, S. M. A.; Breggia, A.; Ahmad, B.; Christman, R.; T, R. S.; Amal, S. Multi-Scale Digital Pathology Patch Level Prostate Cancer Grading Using Deep Learning: Use Case Evaluation of DiagSet Dataset. Preprints2024, 2024050455. https://doi.org/10.20944/preprints202405.0455.v1
Kondejkar, T.; Al-Heejawi, S. M. A.; Breggia, A.; Ahmad, B.; Christman, R.; T, R. S.; Amal, S. Multi-Scale Digital Pathology Patch Level Prostate Cancer Grading Using Deep Learning: Use Case Evaluation of DiagSet Dataset. Preprints 2024, 2024050455. https://doi.org/10.20944/preprints202405.0455.v1
Kondejkar, T.; Al-Heejawi, S. M. A.; Breggia, A.; Ahmad, B.; Christman, R.; T, R. S.; Amal, S. Multi-Scale Digital Pathology Patch Level Prostate Cancer Grading Using Deep Learning: Use Case Evaluation of DiagSet Dataset. Preprints2024, 2024050455. https://doi.org/10.20944/preprints202405.0455.v1
APA Style
Kondejkar, T., Al-Heejawi, S. M. A., Breggia, A., Ahmad, B., Christman, R., T, R. S., & Amal, S. (2024). Multi-Scale Digital Pathology Patch Level Prostate Cancer Grading Using Deep Learning: Use Case Evaluation of DiagSet Dataset. Preprints. https://doi.org/10.20944/preprints202405.0455.v1
Chicago/Turabian Style
Kondejkar, T., Ryan Stephen T and Saeed Amal. 2024 "Multi-Scale Digital Pathology Patch Level Prostate Cancer Grading Using Deep Learning: Use Case Evaluation of DiagSet Dataset" Preprints. https://doi.org/10.20944/preprints202405.0455.v1
Abstract
Prostate Cancer remains a prevalent health concern, emphasizing the critical need for early diagnosis and precise treatment strategies to mitigate mortality rates. Accurate prediction of cancer grade is paramount for timely interventions. This paper introduces an approach to prostate cancer grading, framing it as a classification problem. Leveraging ResNet models on multi-scale patch-level digital pathology and the Diagset dataset, the proposed method demonstrates notable success, achieving an accuracy of 0.999 in identifying clinically significant prostate cancer. The study contributes to the evolving landscape of cancer diagnostics, offering a promising avenue for improved grading accuracy and, consequently, more effective treatment planning. By integrating innovative deep-learning techniques with comprehensive datasets, our approach presents a step forward in the pursuit of personalized and targeted cancer care.
Keywords
machine learning; prostate cancer classification; health care; histopathological images
Subject
Medicine and Pharmacology, Pathology and Pathobiology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
