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20232025
most citedExamining Modality Incongruity in Multimodal Federated Learning for Medical Vision and Language-based Disease Detection

2 citations · 3 across the 6 of their papers we have counts for

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5 papers · 1 filter

cs.CV2025

MCAT: Visual Query-Based Localization of Standard Anatomical Clips in Fetal Ultrasound Videos Using Multi-Tier Class-Aware Token Transformer

Divyanshu Mishra, Pramit Saha, He Zhao +4

Accurate standard plane acquisition in fetal ultrasound (US) videos is crucial for fetal growth assessment, anomaly detection, and adherence to clinical guidelines. However, manual…

cs.CV2025

Self-supervised Normality Learning and Divergence Vector-guided Model Merging for Zero-shot Congenital Heart Disease Detection in Fetal Ultrasound Videos

Pramit Saha, Divyanshu Mishra, Netzahualcoyotl Hernandez-Cruz +4

Congenital Heart Disease (CHD) is one of the leading causes of fetal mortality, yet the scarcity of labeled CHD data and strict privacy regulations surrounding fetal ultrasound (US…

cs.CV20241 cited

FedPIA -- Permuting and Integrating Adapters leveraging Wasserstein Barycenters for Finetuning Foundation Models in Multi-Modal Federated Learning

Pramit Saha, Divyanshu Mishra, Felix Wagner +2

Large Vision-Language Models typically require large text and image datasets for effective fine-tuning. However, collecting data from various sites, especially in healthcare, is ch…

cs.CV2024

FOCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics

Pramit Saha, Felix Wagner, Divyanshu Mishra +5

Effective training of large Vision-Language Models (VLMs) on resource-constrained client devices in Federated Learning (FL) requires the usage of parameter-efficient fine-tuning (P…

cs.CV2023

Dual Conditioned Diffusion Models for Out-Of-Distribution Detection: Application to Fetal Ultrasound Videos

Divyanshu Mishra, He Zhao, Pramit Saha +2

Out-of-distribution (OOD) detection is essential to improve the reliability of machine learning models by detecting samples that do not belong to the training distribution. Detecti…