10 citations · 30 across the 75 of their papers we have counts for
15 papers · 2 filters
FedSECA: Sign Election and Coordinate-wise Aggregation of Gradients for Byzantine Tolerant Federated Learning
Joseph Geo Benjamin, Mothilal Asokan, Mohammad Yaqub +1
One of the most common defense strategies against Byzantine clients in federated learning (FL) is to employ a robust aggregator mechanism that makes the training more resilient. Wh…
Y-CA-Net: A Convolutional Attention Based Network for Volumetric Medical Image Segmentation
Muhammad Hamza Sharif, Muzammal Naseer, Mohammad Yaqub +2
Recent attention-based volumetric segmentation (VS) methods have achieved remarkable performance in the medical domain which focuses on modeling long-range dependencies. However, f…
Can language-guided unsupervised adaptation improve medical image classification using unpaired images and texts?
Umaima Rahman, Raza Imam, Mohammad Yaqub +2
In medical image classification, supervised learning is challenging due to the scarcity of labeled medical images. To address this, we leverage the visual-textual alignment within…
A Federated Learning-Friendly Approach for Parameter-Efficient Fine-Tuning of SAM in 3D Segmentation
Mothilal Asokan, Joseph Geo Benjamin, Mohammad Yaqub +1
Adapting foundation models for medical image analysis requires finetuning them on a considerable amount of data because of extreme distribution shifts between natural (source) data…
Continual Learning in Medical Imaging: A Survey and Practical Analysis
Mohammad Areeb Qazi, Anees Ur Rehman Hashmi, Santosh Sanjeev +4
Deep Learning has shown great success in reshaping medical imaging, yet it faces numerous challenges hindering widespread application. Issues like catastrophic forgetting and distr…
DynaMMo: Dynamic Model Merging for Efficient Class Incremental Learning for Medical Images
Mohammad Areeb Qazi, Ibrahim Almakky, Anees Ur Rehman Hashmi +2
Continual learning, the ability to acquire knowledge from new data while retaining previously learned information, is a fundamental challenge in machine learning. Various approache…
Envisioning MedCLIP: A Deep Dive into Explainability for Medical Vision-Language Models
Anees Ur Rehman Hashmi, Dwarikanath Mahapatra, Mohammad Yaqub
Explaining Deep Learning models is becoming increasingly important in the face of daily emerging multimodal models, particularly in safety-critical domains like medical imaging. Ho…
TiBiX: Leveraging Temporal Information for Bidirectional X-ray and Report Generation
Santosh Sanjeev, Fadillah Adamsyah Maani, Arsen Abzhanov +4
With the emergence of vision language models in the medical imaging domain, numerous studies have focused on two dominant research activities: (1) report generation from Chest X-ra…
SurvRNC: Learning Ordered Representations for Survival Prediction using Rank-N-Contrast
Numan Saeed, Muhammad Ridzuan, Fadillah Adamsyah Maani +3
Predicting the likelihood of survival is of paramount importance for individuals diagnosed with cancer as it provides invaluable information regarding prognosis at an early stage.…
MedPromptX: Grounded Multimodal Prompting for Chest X-ray Diagnosis
Mai A. Shaaban, Adnan Khan, Mohammad Yaqub
Chest X-ray images are commonly used for predicting acute and chronic cardiopulmonary conditions, but efforts to integrate them with structured clinical data face challenges due to…
FissionFusion: Fast Geometric Generation and Hierarchical Souping for Medical Image Analysis
Santosh Sanjeev, Nuren Zhaksylyk, Ibrahim Almakky +3
The scarcity of well-annotated medical datasets requires leveraging transfer learning from broader datasets like ImageNet or pre-trained models like CLIP. Model soups averages mult…
HuLP: Human-in-the-Loop for Prognosis
Muhammad Ridzuan, Mai Kassem, Numan Saeed +2
This paper introduces HuLP, a Human-in-the-Loop for Prognosis model designed to enhance the reliability and interpretability of prognostic models in clinical contexts, especially w…
MedMerge: Merging Models for Effective Transfer Learning to Medical Imaging Tasks
Ibrahim Almakky, Santosh Sanjeev, Anees Ur Rehman Hashmi +3
Transfer learning has become a powerful tool to initialize deep learning models to achieve faster convergence and higher performance. This is especially useful in the medical imagi…
CoReEcho: Continuous Representation Learning for 2D+time Echocardiography Analysis
Fadillah Adamsyah Maani, Numan Saeed, Aleksandr Matsun +1
Deep learning (DL) models have been advancing automatic medical image analysis on various modalities, including echocardiography, by offering a comprehensive end-to-end training pi…
ConDiSR: Contrastive Disentanglement and Style Regularization for Single Domain Generalization
Aleksandr Matsun, Numan Saeed, Fadillah Adamsyah Maani +1
Medical data often exhibits distribution shifts, which cause test-time performance degradation for deep learning models trained using standard supervised learning pipelines. This c…