Tahsin Kurç

Evaluation of nucleus segmentation in digital pathology images through large scale image synthesis

2017 · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE

Digital histopathology images with more than 1 Gigapixel are drawing more and more attention in clinical, biomedical research, and computer vision fields. Among the multiple observable features spanning multiple scales in the pathology images, the …

Unsupervised Histopathology Image Synthesis

2017 · arXiv (Cornell University)

Hematoxylin and Eosin stained histopathology image analysis is essential for the diagnosis and study of complicated diseases such as cancer. Existing state-of-the-art approaches demand extensive amount of supervised training data from trained pathologists. In this …

Building Efficient CNN Architectures for Histopathology Images Analysis: A Case-Study in Tumor-Infiltrating Lymphocytes Classification

2022 · Frontiers in Medicine

Background: Deep learning methods have demonstrated remarkable performance in pathology image analysis, but they are computationally very demanding. The aim of our study is to reduce their computational cost to enable their use with large …

Evaluating histopathology transfer learning with ChampKit

2022 · arXiv (Cornell University)

Histopathology remains the gold standard for diagnosis of various cancers. Recent advances in computer vision, specifically deep learning, have facilitated the analysis of histopathology images for various tasks, including immune cell detection and microsatellite instability …

Topology-Guided Multi-Class Cell Context Generation for Digital Pathology

2023 · arXiv (Cornell University)

In digital pathology, the spatial context of cells is important for cell classification, cancer diagnosis and prognosis. To model such complex cell context, however, is challenging. Cells form different mixtures, lineages, clusters and holes. To …