3 citations · 9 across the 8 of their papers we have counts for
6 papers · 1 filter
CORTEX: A Structured Reasoning Benchmark for Trustworthy 3D Chest CT MLLMs
Hashmat Shadab Malik, Anees Ur Rehman Hashmi, Numan Saeed +3
Reasoning in multimodal large language models (MLLMs) has shown strong promise in medical imaging. However, this reasoning is usually free-form text judged only by its final answer…
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…
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…
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…