no code implementations • 2 Feb 2024 • Yasser H. Khalil, Amir H. Estiri, Mahdi Beitollahi, Nader Asadi, Sobhan Hemati, Xu Li, Guojun Zhang, Xi Chen
In the realm of real-world devices, centralized servers in Federated Learning (FL) present challenges including communication bottlenecks and susceptibility to a single point of failure.
no code implementations • 2 Feb 2024 • Mahdi Beitollahi, Alex Bie, Sobhan Hemati, Leo Maxime Brunswic, Xu Li, Xi Chen, Guojun Zhang
This paper introduces FedPFT (Federated Learning with Parametric Feature Transfer), a methodology that harnesses the transferability of foundation models to enhance both accuracy and communication efficiency in one-shot FL.
no code implementations • 6 Jan 2024 • Isaiah Lahr, Saghir Alfasly, Peyman Nejat, Jibran Khan, Luke Kottom, Vaishnavi Kumbhar, Areej Alsaafin, Abubakr Shafique, Sobhan Hemati, Ghazal Alabtah, Nneka Comfere, Dennis Murphee, Aaron Mangold, Saba Yasir, Chady Meroueh, Lisa Boardman, Vijay H. Shah, Joaquin J. Garcia, H. R. Tizhoosh
Searching for similar images in archives of histology and histopathology images is a crucial task that may aid in patient matching for various purposes, ranging from triaging and diagnosis to prognosis and prediction.
no code implementations • 8 Dec 2023 • Sobhan Hemati, Mahdi Beitollahi, Amir Hossein Estiri, Bassel Al Omari, Xi Chen, Guojun Zhang
The VRM reduces the estimation error in ERM by replacing the point-wise kernel estimates with a more precise estimation of true data distribution that reduces the gap between data points \textbf{within each domain}.
no code implementations • 14 Sep 2023 • Saghir Alfasly, Peyman Nejat, Sobhan Hemati, Jibran Khan, Isaiah Lahr, Areej Alsaafin, Abubakr Shafique, Nneka Comfere, Dennis Murphree, Chady Meroueh, Saba Yasir, Aaron Mangold, Lisa Boardman, Vijay Shah, Joaquin J. Garcia, H. R. Tizhoosh
Recently, several studies have reported on the fine-tuning of foundation models for image-text modeling in the field of medicine, utilizing images from online data sources such as Twitter and PubMed.
1 code implementation • ICCV 2023 • Sobhan Hemati, Guojun Zhang, Amir Estiri, Xi Chen
We validate the OOD generalization ability of proposed methods in different scenarios, including transferability, severe correlation shift, label shift and diversity shift.
no code implementations • 24 Mar 2023 • Vahid Partovi Nia, Guojun Zhang, Ivan Kobyzev, Michael R. Metel, Xinlin Li, Ke Sun, Sobhan Hemati, Masoud Asgharian, Linglong Kong, Wulong Liu, Boxing Chen
Deep models are dominating the artificial intelligence (AI) industry since the ImageNet challenge in 2012.
no code implementations • 29 Aug 2022 • Sobhan Hemati, Shivam Kalra, Morteza Babaie, H. R. Tizhoosh
Learning suitable Whole slide images (WSIs) representations for efficient retrieval systems is a non-trivial task.
no code implementations • 1 Oct 2021 • Sobhan Hemati, H. R. Tizhoosh
We relax the orthogonality constraint on the projection in a PCA-formulation and regularize this by a quantization term.
no code implementations • 11 Jun 2021 • Shivam Kalra, Mohammed Adnan, Sobhan Hemati, Taher Dehkharghanian, Shahryar Rahnamayan, Hamid Tizhoosh
The feature extractor model is fine-tuned using hierarchical target labels of WSIs, i. e., anatomic site and primary diagnosis.
1 code implementation • 24 Dec 2020 • Sobhan Hemati, Mohammad Hadi Mehdizavareh, Shojaeddin Chenouri, Hamid R Tizhoosh
The problem with all existing relaxation methods is resorting to one or more additional auxiliary variables to attain high quality binary codes while relaxing the problem.