World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
56
Citations
10800
World Ranking
4126
National Ranking
1951

Overview

Tahsin Kurc is affiliated with Stony Brook University in the United States. Their research spans the interdisciplinary fields of Computer Science and Medicine, specifically emphasizing Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, and Oncology.

Their main fields of study cover Computer Science with 75 publications and Medicine with 70 publications. Subfields include Artificial Intelligence (43 publications), Radiology, Nuclear Medicine and Imaging (35), Computer Vision and Pattern Recognition (24), Oncology (20), and Biophysics (15).

Tahsin Kurc's work addresses key topics such as AI in cancer detection, Radiomics and Machine Learning in Medical Imaging, and Cell Image Analysis Techniques. Other focus areas include Digital Imaging for Blood Diseases, Cancer Genomics and Diagnostics, Artificial Intelligence in Healthcare and Education, and Cancer Immunotherapy and Biomarkers.

Some of the most frequent publication venues for Tahsin Kurc include arXiv (Cornell University) with 15 publications, Cancer Research with 4, Computer Methods and Programs in Biomedicine with 3, Zenodo (CERN European Organization for Nuclear Research) with 3, and IEEE Journal of Biomedical and Health Informatics with 2.

Frequent co-authors working alongside Tahsin Kurc are Joel Saltz, Rajarsi Gupta, Shahira Abousamra, Dimitris Samaras, and Spyridon Bakas, with collaboration counts ranging from 13 to 60 joint works.

Significant recent papers include:

  • AI in Medical Imaging Informatics: Current Challenges and Future Directions, 2020, IEEE Journal of Biomedical and Health Informatics
  • Metrics reloaded: recommendations for image analysis validation, 2024, Nature Methods
  • ADIOS 2: The Adaptable Input Output System. A framework for high-performance data management, 2020, SoftwareX
  • Understanding metric-related pitfalls in image analysis validation, 2024, Nature Methods
  • Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor-Infiltrating Lymphocytes in Invasive Breast Cancer, 2020, American Journal Of Pathology

Best Publications

  • Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images.

    Joel Saltz;Rajarsi Gupta;Le Hou;Tahsin Kurc

  • AI in Medical Imaging Informatics: Current Challenges and Future Directions

    Andreas S. Panayides;Amir Amini;Nenad D. Filipovic;Ashish Sharma

  • Federated learning enables big data for rare cancer boundary detection

    Unknown

  • Methods for Segmentation and Classification of Digital Microscopy Tissue Images.

    Quoc Dang Vu;Simon Graham;Tahsin Kurc;Minh Nguyen Nhat To

  • Distributed processing of very large datasets with DataCutter

    Michael D. Beynon;Tahsin Kurc;Umit Catalyurek;Chialin Chang

  • Twenty Years of Digital Pathology: An Overview of the Road Travelled, What is on the Horizon, and the Emergence of Vendor-Neutral Archives.

    Liron Pantanowitz;Ashish Sharma;Alexis B Carter;Tahsin Kurc

  • DataCutter: Middleware for Filtering Very Large Scientific Datasets on Archival Storage Systems.

    Michael D. Beynon;Renato Ferreira;Tahsin M. Kurç;Alan Sussman

  • caGrid: design and implementation of the core architecture of the cancer biomedical informatics grid

    Joel Saltz;Scott Oster;Shannon Hastings;Stephen Langella

  • Cancer Digital Slide Archive: an informatics resource to support integrated in silico analysis of TCGA pathology data

    David A. Gutman;Jake Cobb;Dhananjaya Somanna;Yuna Park

  • ADIOS 2: The Adaptable Input Output System. A framework for high-performance data management

    William F. Godoy;Norbert Podhorszki;Ruonan Wang;Chuck Atkins

  • The Proneural Molecular Signature Is Enriched in Oligodendrogliomas and Predicts Improved Survival among Diffuse Gliomas

    Lee Cooper;David Andrew Gutman;Qi Long;Brent Johnson

  • Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images

    Le Hou;Vu Nguyen;Ariel B. Kanevsky;Ariel B. Kanevsky;Dimitris Samaras

  • caGrid 1.0: an enterprise Grid infrastructure for biomedical research.

    Scott Oster;Stephen Langella;Shannon Hastings;David Ervin

  • The virtual microscope

    U. Catalyurek;M.D. Beynon;Chialin Chang;T. Kurc

  • Robust Histopathology Image Analysis: To Label or to Synthesize?

    Le Hou;Ayush Agarwal;Dimitris Samaras;Tahsin M. Kurc

  • Malignant-lesion segmentation using 4D co-occurrence texture analysis applied to dynamic contrast-enhanced magnetic resonance breast image data.

    Brent J. Woods;Bradley D. Clymer;Tahsin Kurc;Johannes T. Heverhagen

  • Integrated morphologic analysis for the identification and characterization of disease subtypes.

    Lee A D Cooper;Jun Kong;David Andrew Gutman;Fusheng Wang

  • The Emergence of Pathomics

    Rajarsi Gupta;Tahsin Kurc;Ashish Sharma;Jonas S. Almeida

  • Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor-Infiltrating Lymphocytes in Invasive Breast Cancer.

    Han Le;Rajarsi Gupta;Rajarsi Gupta;Le Hou;Shahira Abousamra

  • Processing large-scale multi-dimensional data in parallel and distributed environments

    Michael Beynon;Chialin Chang;Umit Catalyurek;Tahsin Kurc

  • Image processing for the grid: a toolkit for building grid-enabled image processing applications

    S. Hastings;T. Kurc;S. Langella;U. Catalyurek

  • Visualization of large data sets with the Active Data Repository

    T. Kurc;U. Catalyurek;Chialin Chang;A. Sussman

  • Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images

    Le Hou;Vu Nguyen;Dimitris Samaras;Tahsin M. Kurc

Frequent Co-Authors

Joel H. Saltz
Joel H. Saltz Stony Brook University
Ümit V. Çatalyürek
Ümit V. Çatalyürek Georgia Institute of Technology
Lee Cooper
Lee Cooper Northwestern University
Dimitris Samaras
Dimitris Samaras Stony Brook University
Fusheng Wang
Fusheng Wang Stony Brook University
P. Sadayappan
P. Sadayappan University of Utah
Scott Klasky
Scott Klasky Oak Ridge National Laboratory
Alan Sussman
Alan Sussman University of Maryland, College Park
Norbert Podhorszki
Norbert Podhorszki Oak Ridge National Laboratory
Carlos S. Moreno
Carlos S. Moreno Emory University

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