173 citations · 300 across the 13 of their papers we have counts for
13 papers
Paired Uterine Whole-Slide Images and Pathology Reports for Multimodal Computational Pathology
Han Li, Jingsong Liu, Ayako Ura +14
Uterine diseases represent an important category of gynecologic pathology and require accurate histopathological assessment for diagnosis and treatment planning. Whole-slide images…
The Mean is the Mirage: Entropy-Adaptive Model Merging under Heterogeneous Domain Shifts in Medical Imaging
Sameer Ambekar, Reza Nasirigerdeh, Peter J. Schuffler +3
Model merging under unseen test-time distribution shifts often renders naive strategies, such as mean averaging unreliable. This challenge is especially acute in medical imaging, w…
Improved Localized Machine Unlearning Through the Lens of Memorization
Reihaneh Torkzadehmahani, Reza Nasirigerdeh, Georgios Kaissis +3
Machine unlearning refers to removing the influence of a specified subset of training data from a machine learning model, efficiently, after it has already been trained. This is im…
Machine Unlearning for Medical Imaging
Reza Nasirigerdeh, Nader Razmi, Julia A. Schnabel +2
Machine unlearning is the process of removing the impact of a particular set of training samples from a pretrained model. It aims to fulfill the "right to be forgotten", which gran…
Kernel Normalized Convolutional Networks for Privacy-Preserving Machine Learning
Reza Nasirigerdeh, Javad Torkzadehmahani, Daniel Rueckert +1
Normalization is an important but understudied challenge in privacy-related application domains such as federated learning (FL), differential privacy (DP), and differentially priva…
Label Noise-Robust Learning using a Confidence-Based Sieving Strategy
Reihaneh Torkzadehmahani, Reza Nasirigerdeh, Daniel Rueckert +1
In learning tasks with label noise, improving model robustness against overfitting is a pivotal challenge because the model eventually memorizes labels, including the noisy ones. I…
Kernel Normalized Convolutional Networks
Reza Nasirigerdeh, Reihaneh Torkzadehmahani, Daniel Rueckert +1
Existing convolutional neural network architectures frequently rely upon batch normalization (BatchNorm) to effectively train the model. BatchNorm, however, performs poorly with sm…
The FeatureCloud AI Store for Federated Learning in Biomedicine and Beyond
Julian Matschinske, Julian Späth, Reza Nasirigerdeh +29
Machine Learning (ML) and Artificial Intelligence (AI) have shown promising results in many areas and are driven by the increasing amount of available data. However, this data is o…
HyFed: A Hybrid Federated Framework for Privacy-preserving Machine Learning
Reza Nasirigerdeh, Reihaneh Torkzadehmahani, Julian Matschinske +3
Federated learning (FL) enables multiple clients to jointly train a global model under the coordination of a central server. Although FL is a privacy-aware paradigm, where raw data…
Flimma: a federated and privacy-preserving tool for differential gene expression analysis
Olga Zolotareva, Reza Nasirigerdeh, Julian Matschinske +9
Aggregating transcriptomics data across hospitals can increase sensitivity and robustness of differential expression analyses, yielding deeper clinical insights. As data exchange i…
Federated Multi-Mini-Batch: An Efficient Training Approach to Federated Learning in Non-IID Environments
Reza Nasirigerdeh, Mohammad Bakhtiari, Reihaneh Torkzadehmahani +4
Federated learning has faced performance and network communication challenges, especially in the environments where the data is not independent and identically distributed (IID) ac…
Privacy-preserving Artificial Intelligence Techniques in Biomedicine
Reihaneh Torkzadehmahani, Reza Nasirigerdeh, David B. Blumenthal +22
Artificial intelligence (AI) has been successfully applied in numerous scientific domains. In biomedicine, AI has already shown tremendous potential, e.g. in the interpretation of…
Exploring the SARS-CoV-2 virus-host-drug interactome for drug repurposing
Sepideh Sadegh, Julian Matschinske, David B. Blumenthal +14
Coronavirus Disease-2019 (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. It was first identified in Wuhan, China, and has since spread causing a global pandemic…