Pathology Commons^™

Development Of An Online Teaching Platform To Improve Access To Postgraduate Pathology Training In Sub-Saharan Africa, Richard Byers, Anita Byers, Chibamba Mumba, Angela Mutuku, Jennifer Singer-Rupp, Michael Wilson, Kenneth Fleming, Shahin Sayed

Pathology, East Africa

Background: Resource barriers to the provision of accessible training in cancer diagnosis in lower- and middle-income countries (LMICs) limit the potential of African health systems. Long-term provision via teaching visits from senior pathologists and trainee foreign placements is unsustainable due to the prohibitive costs of travel and subsistence. Emerging eLearning methods would allow pathologists to be trained by experts in a cheaper, more efficient, and more scalable way.

Purpose: This study aimed to develop an online teaching platform, starting with hematopathology, for trainee pathologists in sub-Saharan Africa, initially in Nairobi, Kenya, and Lusaka, Zambia.

Methods: Course materials …

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

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 …

Computational Modeling For Abnormal Brain Tissue Segmentation, Brain Tumor Tracking, And Grading, Syed Mohammad Shamin Reza

Electrical & Computer Engineering Theses & Dissertations

This dissertation proposes novel texture feature-based computational models for quantitative analysis of abnormal tissues in two neurological disorders: brain tumor and stroke. Brain tumors are the cells with uncontrolled growth in the brain tissues and one of the major causes of death due to cancer. On the other hand, brain strokes occur due to the sudden interruption of the blood supply which damages the normal brain tissues and frequently causes death or persistent disability. Clinical management of these brain tumors and stroke lesions critically depends on robust quantitative analysis using different imaging modalities including Magnetic Resonance (MR) and Digital Pathology …