Sobhan Hemati
University of Waterloo
23 Papers
13 Citations
Sobhan Hemati is an academic researcher from University of Waterloo. The author has contributed to research in topics: Computer science & Feature (computer vision). The author has an hindex of 2, co-authored 12 publications. Previous affiliations of Sobhan Hemati include University of Tehran.
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Papers
Fine-Tuning and training of densenet for histopathology image representation using TCGA diagnostic slides.
Abtin Riasatian,Morteza Babaie,Danial Maleki,Shivam Kalra,Mojtaba Valipour,Sobhan Hemati,Manit Zaveri,Amir Safarpoor,Sobhan Shafiei,Mehdi Afshari,Maral Rasoolijaberi,Milad Sikaroudi,Mohd Adnan,Sultaan Shah,Charles Choi,Savvas Damaskinos,Clinton J. V. Campbell,Phedias Diamandis,Liron Pantanowitz,Hany Kashani,Ali Ghodsi,Hamid R. Tizhoosh +21 more
TL;DR: KimiaNet as discussed by the authors employs the topology of the DenseNet with four dense blocks, fine-tuned and trained with histopathology images in different configurations, using high-cellularity mosaic approach to enable the usage of weak labels of 7126 whole slide images of formalin-fixed paraffin-embedded human pathology samples publicly available through The Cancer Genome Atlas (TCGA) repository.
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A self-supervised contrastive learning approach for whole slide image representation in digital pathology
TL;DR: Li et al. as discussed by the authors proposed a self-supervised learning scheme based on the available primary site information to increase the robustness of the representations for WSI classification and search for both pretext and downstream tasks.
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Enhancing performance of subject-specific models via subject-independent information for SSVEP-based BCIs.
TL;DR: In this paper, a new method based on canonical correlation analysis (CCA) was proposed to integrate subject-specific models and subject-independent information and enhance BCI performance, and the proposed method outperformed extended CCA in all conditions and TRCA for time windows greater than 0.3 s.
Pay Attention with Focus: A Novel Learning Scheme for Classification of Whole Slide Images
Shivam Kalra,Mohammed Adnan,Sobhan Hemati,Taher Dehkharghanian,Shahryar Rahnamayan,Hamid R. Tizhoosh +5 more
- 27 Sep 2021
TL;DR: Wang et al. as discussed by the authors proposed a two-stage approach to extract representative patches (called mosaic) from a WSI, each patch of a mosaic is encoded to a feature vector using a deep network.
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When is a Foundation Model a Foundation Model
Saghir Ahmed Saghir Alfasly,Peyman Nejat,Sobhan Hemati,Jibran Khan,Isaiah Lahr,Areej Alsaafin,Abubakr Shafique,Nneka I. Comfere,Dennis Murphree,Chady Meroueh,Saba Yasir,Aaron Mangold,Lisa Boardman,Vijay Shah,Joaquin J. Garcia,Hamid R. Tizhoosh +15 more
TL;DR: Through validation, it is observed that the representations generated by foundation models exhibit inferior performance in retrieval tasks within digital pathology when compared to those generated by significantly smaller, conventional deep networks.
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