Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Analytical, Diagnostic and Therapeutic Techniques and Equipment (1)
- Applied Mathematics (1)
- Artificial Intelligence and Robotics (1)
- Computer Sciences (1)
- Databases and Information Systems (1)
-
- Dentistry (1)
- Diseases (1)
- Investigative Techniques (1)
- Medical Pathology (1)
- Medical Sciences (1)
- Neoplasms (1)
- Numerical Analysis and Computation (1)
- Numerical Analysis and Scientific Computing (1)
- Oral Biology and Oral Pathology (1)
- Other Analytical, Diagnostic and Therapeutic Techniques and Equipment (1)
- Physical Sciences and Mathematics (1)
- Institution
- Publication Type
Articles 1 - 2 of 2
Full-Text Articles in Pathology
Pitfalls In Machine Learning-Based Assessment Of Tumor-Infiltrating Lymphocytes In Breast Cancer: A Report Of The International Immuno-Oncology Biomarker Working Group On Breast Cancer, Jeppe Thagaard, Glenn Broeckx, Chowdhury Arif Jahangir, Sara Verbandt, Rajarsi Gupta, Reena Khiroya, Khalid Abduljabbar, Gabriela Acosta Haab, Balazs Acs, Shahin Sayed
Pitfalls In Machine Learning-Based Assessment Of Tumor-Infiltrating Lymphocytes In Breast Cancer: A Report Of The International Immuno-Oncology Biomarker Working Group On Breast Cancer, Jeppe Thagaard, Glenn Broeckx, Chowdhury Arif Jahangir, Sara Verbandt, Rajarsi Gupta, Reena Khiroya, Khalid Abduljabbar, Gabriela Acosta Haab, Balazs Acs, Shahin Sayed
Pathology, East Africa
Abstract: The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL …
Head And Neck Tumor Histopathological Image Representation With Pre- Trained Convolutional Neural Network And Vision Transformer, Ranny Rahaningrum Herdiantoputri, Daisuke Komura, Tohru Ikeda, Shumpei Ishikawa
Head And Neck Tumor Histopathological Image Representation With Pre- Trained Convolutional Neural Network And Vision Transformer, Ranny Rahaningrum Herdiantoputri, Daisuke Komura, Tohru Ikeda, Shumpei Ishikawa
Journal of Dentistry Indonesia
Image representation via machine learning is an approach to quantitatively represent histopathological images of head and neck tumors for future applications of artificial intelligence-assisted pathological diagnosis systems. Objective: This study compares image representations produced by a pre-trained convolutional neural network (VGG16) to those produced by a vision transformer (ViT-L/14) in terms of the classification performance of head and neck tumors. Methods: W hole-slide images of five oral t umor categories (n = 319 cases) were analyzed. Image patches were created from manually annotated regions at 4096, 2048, and 1024 pixels and rescaled to 256 pixels. Image representations were …