Nature Search

Decentralized federated learning through proxy model sharing

Federated learning enables multi-institutional collaborations on decentralized data with improved privacy protection. Here, authors propose a new scheme for decentralized federated learning with much less communication overhead and stronger privacy.

Shivam Kalra
Junfeng Wen
H. R. Tizhoosh
ResearchOpen Access22 May 2023
Nature Communications

Volume: 14, P: 1-10
Generalization of vision pre-trained models for histopathology

Milad Sikaroudi
Maryam Hosseini
H. R. Tizhoosh
ResearchOpen Access13 Apr 2023
Scientific Reports

Volume: 13, P: 1-14
Federated learning and differential privacy for medical image analysis

Mohammed Adnan
Shivam Kalra
Hamid R. Tizhoosh
ResearchOpen Access04 Feb 2022
Scientific Reports

Volume: 12, P: 1-10
Validation of histopathology foundation models through whole slide image retrieval

Saghir Alfasly
Ghazal Alabtah
H. R. Tizhoosh
ResearchOpen Access01 Feb 2025
Scientific Reports

Volume: 15, P: 1-8
A self-supervised framework for cross-modal search in histopathology archives using scale harmonization

Danial Maleki
Shahryar Rahnamayan
H. R. Tizhoosh
ResearchOpen Access27 Apr 2024
Scientific Reports

Volume: 14, P: 1-17
Creating an atlas of normal tissue for pruning WSI patching through anomaly detection

Peyman Nejat
Areej Alsaafin
H. R. Tizhoosh
ResearchOpen Access16 Feb 2024
Scientific Reports

Volume: 14, P: 1-15
Learning to predict RNA sequence expressions from whole slide images with applications for search and classification

tRNAsformer enables prediction of bulk RNA-seq from histological slides using machine learning approaches.

Areej Alsaafin
Amir Safarpoor
H. R. Tizhoosh
ResearchOpen Access22 Mar 2023
Communications Biology

Volume: 6, P: 1-9
Automated bone marrow cytology using deep learning to generate a histogram of cell types

Moosavi Tayebi et al. develop a deep learning-based computational pathology tool for automated bone marrow cytology from whole slide images. Their approach generates a histogram of cell types present within the bone marrow aspirate to aid in diagnostic haematopathology.

Rohollah Moosavi Tayebi
Youqing Mu
Clinton J. V. Campbell
ResearchOpen Access20 Apr 2022
Communications Medicine

Volume: 2, P: 1-14
A BERT model generates diagnostically relevant semantic embeddings from pathology synopses with active learning

Mu et al. utilize a deep learning natural language processing model as part of an active learning approach to extract diagnostically relevant semantic information from bone marrow pathology synopses. Their findings demonstrate the potential for artificial intelligence in assisting clinicians in assessing, cataloging and triaging medical text datasets such as pathology synopses.

Youqing Mu
Hamid R. Tizhoosh
Clinton J. V. Campbell
ResearchOpen Access05 Jul 2021
Communications Medicine

Volume: 1, P: 1-13

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