10 citations · 30 across the 75 of their papers we have counts for
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FETAL-GAUGE: A Benchmark for Assessing Vision-Language Models in Fetal Ultrasound
Hussain Alasmawi, Numan Saeed, Mohammad Yaqub
The growing demand for prenatal ultrasound imaging has intensified a global shortage of trained sonographers, creating barriers to essential fetal health monitoring. Deep learning…
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…
MMRINet: Efficient Mamba-Based Segmentation with Dual-Path Refinement for Low-Resource MRI Analysis
Abdelrahman Elsayed, Ahmed Jaheen, Mohammad Yaqub
Automated brain tumor segmentation in multi-parametric MRI remains a critical yet underserved challenge in resource-constrained clinical settings, where deep 3D networks requiring…
T3: Test-Time Model Merging in VLMs for Zero-Shot Medical Imaging Analysis
Raza Imam, Hu Wang, Dwarikanath Mahapatra +1
In medical imaging, vision-language models face a critical duality: pretrained networks offer broad robustness but lack subtle, modality-specific characteristics, while fine-tuned…
DuPLUS: Dual-Prompt Vision-Language Framework for Universal Medical Image Segmentation and Prognosis
Numan Saeed, Tausifa Jan Saleem, Fadillah Maani +3
Deep learning for medical imaging is hampered by task-specific models that lack generalizability and prognostic capabilities, while existing 'universal' approaches suffer from simp…
CardioBench: Do Echocardiography Foundation Models Generalize Beyond the Lab?
Darya Taratynova, Ahmed Aly, Numan Saeed +1
Foundation models are reshaping medical imaging, yet their application in echocardiography remains limited, hindered by a heavy reliance on private datasets that prevent reproducib…
A Multimodal and Multi-centric Head and Neck Cancer Dataset for Segmentation, Diagnosis and Outcome Prediction
Numan Saeed, Salma Hassan, Shahad Hardan +40
We present a publicly available multimodal dataset for head and neck cancer research, comprising 1123 annotated Positron Emission Tomography/Computed Tomography (PET/CT) studies fr…
Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift
Umaima Rahman, Raza Imam, Mohammad Yaqub +1
Medical vision-language models (VLMs) offer promise for clinical decision support, yet their reliability under distribution shifts remains a major concern for safe deployment. Thes…
Predicting Brain Tumor Response to Therapy using a Hybrid Deep Learning and Radiomics Approach
Daniil Tikhonov, Matheus Scatolin, Mohor Banerjee +7
Accurate evaluation of the response of glioblastoma to therapy is crucial for clinical decision-making and patient management. The Response Assessment in Neuro-Oncology (RANO) crit…
TPA: Temporal Prompt Alignment for Fetal Congenital Heart Defect Classification
Darya Taratynova, Alya Almsouti, Beknur Kalmakhanbet +2
Congenital heart defect (CHD) detection in ultrasound videos is hindered by image noise and probe positioning variability. While automated methods can reduce operator dependence, c…
Advancing Fetal Ultrasound Image Quality Assessment in Low-Resource Settings
Dongli He, Hu Wang, Mohammad Yaqub
Accurate fetal biometric measurements, such as abdominal circumference, play a vital role in prenatal care. However, obtaining high-quality ultrasound images for these measurements…
Not Only Grey Matter: OmniBrain for Robust Multimodal Classification of Alzheimer's Disease
Ahmed Sharshar, Yasser Ashraf, Tameem Bakr +4
Alzheimer's disease affects over 55 million people worldwide and is projected to more than double by 2050, necessitating rapid, accurate, and scalable diagnostics. However, existin…
TransPrune: Token Transition Pruning for Efficient Large Vision-Language Model
Ao Li, Yuxiang Duan, Jinghui Zhang +5
Large Vision-Language Models (LVLMs) have advanced multimodal learning but face high computational costs due to the large number of visual tokens, motivating token pruning to impro…
MOTOR: Multimodal Optimal Transport via Grounded Retrieval in Medical Visual Question Answering
Mai A. Shaaban, Tausifa Jan Saleem, Vijay Ram Papineni +1
Medical visual question answering (MedVQA) plays a vital role in clinical decision-making by providing contextually rich answers to image-based queries. Although vision-language mo…
DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation
Umaima Rahman, Mohammad Yaqub, Dwarikanath Mahapatra
We introduce DiMPLe (Disentangled Multi-Modal Prompt Learning), a novel approach to disentangle invariant and spurious features across vision and language modalities in multi-modal…
On the Robustness of Medical Vision-Language Models: Are they Truly Generalizable?
Raza Imam, Rufael Marew, Mohammad Yaqub
Medical Vision-Language Models (MVLMs) have achieved par excellence generalization in medical image analysis, yet their performance under noisy, corrupted conditions remains largel…
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…
Efficient Parameter Adaptation for Multi-Modal Medical Image Segmentation and Prognosis
Numan Saeed, Shahad Hardan, Muhammad Ridzuan +3
Cancer detection and prognosis relies heavily on medical imaging, particularly CT and PET scans. Deep Neural Networks (DNNs) have shown promise in tumor segmentation by fusing info…
Segmentation-Guided CT Synthesis with Pixel-Wise Conformal Uncertainty Bounds
David Vallmanya Poch, Yorick Estievenart, Elnura Zhalieva +3
Accurate dose calculations in proton therapy rely on high-quality CT images. While planning CTs (pCTs) serve as a reference for dosimetric planning, Cone Beam CT (CBCT) is used thr…
Breaking Down the Hierarchy: A New Approach to Leukemia Classification
Ibraheem Hamdi, Hosam El-Gendy, Ahmed Sharshar +8
The complexities inherent to leukemia, multifaceted cancer affecting white blood cells, pose considerable diagnostic and treatment challenges, primarily due to reliance on laboriou…
Noise is an Efficient Learner for Zero-Shot Vision-Language Models
Raza Imam, Asif Hanif, Jian Zhang +3
Recently, test-time adaptation has garnered attention as a method for tuning models without labeled data. The conventional modus operandi for adapting pre-trained vision-language m…
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…