1 code implementation • 14 Jun 2022 • Jakub R. Kaczmarzyk, Tahsin M. Kurc, Shahira Abousamra, Rajarsi Gupta, Joel H. Saltz, Peter K. Koo
Histopathology remains the gold standard for diagnosis of various cancers.
no code implementations • 30 Mar 2022 • Ujjwal Baid, Sarthak Pati, Tahsin M. Kurc, Rajarsi Gupta, Erich Bremer, Shahira Abousamra, Siddhesh P. Thakur, Joel H. Saltz, Spyridon Bakas
We evaluate the performance of federated learning (FL) in developing deep learning models for analysis of digitized tissue sections.
1 code implementation • 26 Feb 2021 • Sarthak Pati, Siddhesh P. Thakur, İbrahim Ethem Hamamcı, Ujjwal Baid, Bhakti Baheti, Megh Bhalerao, Orhun Güley, Sofia Mouchtaris, David Lang, Spyridon Thermos, Karol Gotkowski, Camila González, Caleb Grenko, Alexander Getka, Brandon Edwards, Micah Sheller, Junwen Wu, Deepthi Karkada, Ravi Panchumarthy, Vinayak Ahluwalia, Chunrui Zou, Vishnu Bashyam, Yuemeng Li, Babak Haghighi, Rhea Chitalia, Shahira Abousamra, Tahsin M. Kurc, Aimilia Gastounioti, Sezgin Er, Mark Bergman, Joel H. Saltz, Yong Fan, Prashant Shah, Anirban Mukhopadhyay, Sotirios A. Tsaftaris, Bjoern Menze, Christos Davatzikos, Despina Kontos, Alexandros Karargyris, Renato Umeton, Peter Mattson, Spyridon Bakas
Deep Learning (DL) has the potential to optimize machine learning in both the scientific and clinical communities.
1 code implementation • 18 Feb 2020 • Le Hou, Rajarsi Gupta, John S. Van Arnam, Yuwei Zhang, Kaustubh Sivalenka, Dimitris Samaras, Tahsin M. Kurc, Joel H. Saltz
To address this, we developed an analysis pipeline that segments nuclei in whole slide tissue images from multiple cancer types with a quality control process.
no code implementations • 13 Dec 2017 • Le Hou, Ayush Agarwal, Dimitris Samaras, Tahsin M. Kurc, Rajarsi R. Gupta, Joel H. Saltz
We propose a unified pipeline that: a) generates a set of initial synthetic histopathology images with paired information about the nuclei such as segmentation masks; b) refines the initial synthetic images through a Generative Adversarial Network (GAN) to reference styles; c) trains a task-specific CNN and boosts the performance of the task-specific CNN with on-the-fly generated adversarial examples.
no code implementations • 3 Apr 2017 • Le Hou, Vu Nguyen, Dimitris Samaras, Tahsin M. Kurc, Yi Gao, Tianhao Zhao, Joel H. Saltz
In this work, we propose a sparse Convolutional Autoencoder (CAE) for fully unsupervised, simultaneous nucleus detection and feature extraction in histopathology tissue images.
no code implementations • 20 Dec 2016 • Veda Murthy, Le Hou, Dimitris Samaras, Tahsin M. Kurc, Joel H. Saltz
Classifying the various shapes and attributes of a glioma cell nucleus is crucial for diagnosis and understanding the disease.
no code implementations • 23 Aug 2016 • Le Hou, Dimitris Samaras, Tahsin M. Kurc, Yi Gao, Joel H. Saltz
In this paper, we propose and apply AAFs on feedforward NNs for regression tasks.
1 code implementation • CVPR 2016 • Le Hou, Dimitris Samaras, Tahsin M. Kurc, Yi Gao, James E. Davis, Joel H. Saltz
However, to recognize cancer subtypes automatically, training a CNN on gigapixel resolution Whole Slide Tissue Images (WSI) is currently computationally impossible.