3 citations · 7 across the 17 of their papers we have counts for
11 papers · 1 filter
Hierarchy-Aware and Anatomy-Guided Learning for Lung Ultrasound Video Classification
Alya Almsouti, Lotfi Mecharbat, Noha Aboukhater +5
Lung ultrasound (LUS) is a bedside tool for assessing pulmonary edema in patients at risk due to heart failure or impaired kidney function. However, automated LUS analysis remains…
MAFM^3: Modular Adaptation of Foundation Models for Multi-Modal Medical AI
Mohammad Areeb Qazi, Munachiso S Nwadike, Ibrahim Almakky +2
Foundational models are trained on extensive datasets to capture the general trends of a domain. However, in medical imaging, the scarcity of data makes pre-training for every doma…
MedNNS: Supernet-based Medical Task-Adaptive Neural Network Search
Lotfi Abdelkrim Mecharbat, Ibrahim Almakky, Martin Takac +1
Deep learning (DL) has achieved remarkable progress in the field of medical imaging. However, adapting DL models to medical tasks remains a significant challenge, primarily due to…
In-Model Merging for Enhancing the Robustness of Medical Imaging Classification Models
Hu Wang, Ibrahim Almakky, Congbo Ma +2
Model merging is an effective strategy to merge multiple models for enhancing model performances, and more efficient than ensemble learning as it will not introduce extra computati…
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…