14 citations · 50 across the 17 of their papers we have counts for
17 papers
WILSON - Multi-magnification composites as a foundation for patient-level pathology analysis and diagnostic text generation
Saghir Alfasly, Wataru Uegami, Sobhan Hemati +17
Pathologists examine tissue sections across multiple magnifications and slides, integrating morphological information to establish a diagnosis. In contrast, most pathology foundati…
Aggregation Schemes for Single-Vector WSI Representation Learning in Digital Pathology
Sobhan Hemati, Ghazal Alabtah, Saghir Alfasly +1
A crucial step to efficiently integrate Whole Slide Images (WSIs) in computational pathology is assigning a single high-quality feature vector, i.e., one embedding, to each WSI. Wi…
Zero-Shot Whole Slide Image Retrieval in Histopathology Using Embeddings of Foundation Models
Saghir Alfasly, Ghazal Alabtah, Sobhan Hemati +2
We have tested recently published foundation models for histopathology for image retrieval. We report macro average of F1 score for top-1 retrieval, majority of top-3 retrievals, a…
A Short Survey on Set-Based Aggregation Techniques for Single-Vector WSI Representation in Digital Pathology
S. Hemati, Krishna R. Kalari, H. R. Tizhoosh
Digital pathology is revolutionizing the field of pathology by enabling the digitization, storage, and analysis of tissue samples as whole slide images (WSIs). WSIs are gigapixel f…
Parametric Feature Transfer: One-shot Federated Learning with Foundation Models
Mahdi Beitollahi, Alex Bie, Sobhan Hemati +4
In one-shot federated learning (FL), clients collaboratively train a global model in a single round of communication. Existing approaches for one-shot FL enhance communication effi…
DFML: Decentralized Federated Mutual Learning
Yasser H. Khalil, Amir H. Estiri, Mahdi Beitollahi +5
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 fa…
Analysis and Validation of Image Search Engines in Histopathology
Isaiah Lahr, Saghir Alfasly, Peyman Nejat +16
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 d…
Cross Domain Generative Augmentation: Domain Generalization with Latent Diffusion Models
Sobhan Hemati, Mahdi Beitollahi, Amir Hossein Estiri +3
Despite the huge effort in developing novel regularizers for Domain Generalization (DG), adding simple data augmentation to the vanilla ERM which is a practical implementation of t…
When is a Foundation Model a Foundation Model
Saghir Alfasly, Peyman Nejat, Sobhan Hemati +13
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…
Understanding Hessian Alignment for Domain Generalization
Sobhan Hemati, Guojun Zhang, Amir Estiri +1
Out-of-distribution (OOD) generalization is a critical ability for deep learning models in many real-world scenarios including healthcare and autonomous vehicles. Recently, differe…
Mathematical Challenges in Deep Learning
Vahid Partovi Nia, Guojun Zhang, Ivan Kobyzev +8
Deep models are dominating the artificial intelligence (AI) industry since the ImageNet challenge in 2012. The size of deep models is increasing ever since, which brings new challe…
Learning Binary and Sparse Permutation-Invariant Representations for Fast and Memory Efficient Whole Slide Image Search
Sobhan Hemati, Shivam Kalra, Morteza Babaie +1
Learning suitable Whole slide images (WSIs) representations for efficient retrieval systems is a non-trivial task. The WSI embeddings obtained from current methods are in Euclidean…
Beyond Neighbourhood-Preserving Transformations for Quantization-Based Unsupervised Hashing
Sobhan Hemati, H. R. Tizhoosh
An effective unsupervised hashing algorithm leads to compact binary codes preserving the neighborhood structure of data as much as possible. One of the most established schemes for…
Pay Attention with Focus: A Novel Learning Scheme for Classification of Whole Slide Images
Shivam Kalra, Mohammed Adnan, Sobhan Hemati +3
Deep learning methods such as convolutional neural networks (CNNs) are difficult to directly utilize to analyze whole slide images (WSIs) due to the large image dimensions. We over…
Fine-Tuning and Training of DenseNet for Histopathology Image Representation Using TCGA Diagnostic Slides
Abtin Riasatian, Morteza Babaie, Danial Maleki +19
Feature vectors provided by pre-trained deep artificial neural networks have become a dominant source for image representation in recent literature. Their contribution to the perfo…
A non-alternating graph hashing algorithm for large scale image search
Sobhan Hemati, Mohammad Hadi Mehdizavareh, Shojaeddin Chenouri +1
In the era of big data, methods for improving memory and computational efficiency have become crucial for successful deployment of technologies. Hashing is one of the most effectiv…
Enhancing performance of subject-specific models via subject-independent information for SSVEP-based BCIs
Mohammad Hadi Mehdizavareh, Sobhan Hemati, Hamid Soltanian-Zadeh
Recently, brain-computer interface (BCI) systems developed based on steady-state visual evoked potential (SSVEP) have attracted much attention due to their high information transfe…