Hamid Tizhoosh

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Professor

Mayo Clinic

United States

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About

Professor Hamid Tizhoosh is a faculty member at Mayo Clinic in the United States. His research focuses on digital pathology, image classification, and the application of machine learning in hematopathology. He has contributed to the understanding of tumor discrimination, immune cell sequence analysis, and federated learning in the context of histopathology. His recent articles highlight innovative approaches in the fields of image representation and clinical applications of spatial omics.

Recent Grants

Grant: Close

Oppositional concepts in population-based problem solving

Open Date: 2012-04-01

Close Date: 2013-03-31

Grant: Close

Oppositional concepts in population-based problem solving

Open Date: 2008-04-01

Close Date: 2012-03-31

Grant: Close

Knowledge-based assistive device for narcoleptic patients

Open Date: 2004-04-01

Close Date: 2007-03-31

Grant: Close

Power quality immunity equipment

Open Date: 2004-04-01

Close Date: 2005-03-31

Grant: Close

Automating observer-dependent enhancement of medical images

Open Date: 2003-04-01

Close Date: 2006-03-31

Articles (16)

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2024

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2024

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2024

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2024

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2024

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2024

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2023

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2023

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2023

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2023

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2023

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2023

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2022

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2022

Year:

2022

Year:

2021

Collaborators (1)

Clinton J. V. Campbell

CANADA

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Hamid Tizhoosh

Research Interests

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About

Recent Grants

Articles (16)

Sequential Patching Lattice for Image Classification and Enquiry

Model-Agnostic Binary Patch Grouping for Bone Marrow Whole Slide Image Representation

Digital pathology and spatial omics in steatohepatitis: Clinical applications and discovery potentials

A Preliminary Investigation into Search and Matching for Tumor Discrimination in World Health Organization Breast Taxonomy Using Deep Networks

Evolutionary multi-objective design of autoencoders for compact representation of histopathology whole slide images

Harmonizing immune cell sequences for computational analysis with large language models

Proportionally Fair Hospital Collaborations in Federated Learning of Histopathology Images

Ranking loss and sequestering learning for reducing image search bias in histopathology

Applied machine learning in hematopathology

Decentralized federated learning through proxy model sharing

Learning to predict RNA sequence expressions from whole slide images with applications for search and classification

Evolutionary Computation in Action: Hyperdimensional Deep Embedding Spaces of Gigapixel Pathology Images

Evolutionary deep feature selection for compact representation of gigapixel images in digital pathology

Multi-Magnification Image Search in Digital Pathology

A non-alternating graph hashing algorithm for large-scale image search

Searching Images for Consensus

Collaborators (1)

Clinton J. V. Campbell

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