10 citations · 30 across the 75 of their papers we have counts for
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BreastMammo and DenseMammo: Benchmarks for Mammography Domain Generalization
Hongyi Pan, Gorkem Durak, Halil Ertugrul Aktas +18
Breast density classification is a critical component of breast cancer risk assessment, yet AI models often struggle to generalize across clinical sites due to vendor-specific acqu…
FUGC: Benchmarking Semi-Supervised Learning Methods for Cervical Segmentation
Jieyun Bai, Yitong Tang, Zihao Zhou +36
Accurate segmentation of cervical structures in transvaginal ultrasound (TVS) is critical for assessing the risk of spontaneous preterm birth (PTB), yet the scarcity of labeled dat…
BRIQA: Balanced Reweighting in Image Quality Assessment of Pediatric Brain MRI
Alya Almsouti, Ainur Khamitova, Darya Taratynova +1
Assessing the severity of artifacts in pediatric brain Magnetic Resonance Imaging (MRI) is critical for diagnostic accuracy, especially in low-field systems where the signal-to-noi…
UNICON: UNIfied CONtinual Learning for Medical Foundational Models
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…
Deep Learning-Based Automated Segmentation of Uterine Myomas
Tausifa Jan Saleem, Mohammad Yaqub
Uterine fibroids (myomas) are the most common benign tumors of the female reproductive system, particularly among women of childbearing age. With a prevalence exceeding 70%, they p…
EMedNeXt: An Enhanced Brain Tumor Segmentation Framework for Sub-Saharan Africa using MedNeXt V2 with Deep Supervision
Ahmed Jaheen, Abdelrahman Elsayed, Damir Kim +8
Brain cancer affects millions worldwide, and in nearly every clinical setting, doctors rely on magnetic resonance imaging (MRI) to diagnose and monitor gliomas. However, the curren…
crossMoDA Challenge: Evolution of Cross-Modality Domain Adaptation Techniques for Vestibular Schwannoma and Cochlea Segmentation from 2021 to 2023
Navodini Wijethilake, Reuben Dorent, Marina Ivory +38
The cross-Modality Domain Adaptation (crossMoDA) challenge series, initiated in 2021 in conjunction with the International Conference on Medical Image Computing and Computer Assist…
SALT: Parameter-Efficient Fine-Tuning via Singular Value Adaptation with Low-Rank Transformation
Abdelrahman Elsayed, Sarim Hashmi, Mohammed Elseiagy +3
The complex nature of medical image segmentation calls for models that are specifically designed to capture detailed, domain-specific features. Large foundation models offer consid…
FetalCLIP: A Visual-Language Foundation Model for Fetal Ultrasound Image Analysis
Fadillah Maani, Numan Saeed, Tausifa Saleem +8
Foundation models are becoming increasingly effective in the medical domain, offering pre-trained models on large datasets that can be readily adapted for downstream tasks. Despite…
PulmoFusion: Advancing Pulmonary Health with Efficient Multi-Modal Fusion
Ahmed Sharshar, Yasser Attia, Mohammad Yaqub +1
Traditional remote spirometry lacks the precision required for effective pulmonary monitoring. We present a novel, non-invasive approach using multimodal predictive models that int…
Optimizing Brain Tumor Segmentation with MedNeXt: BraTS 2024 SSA and Pediatrics
Sarim Hashmi, Juan Lugo, Abdelrahman Elsayed +6
Identifying key pathological features in brain MRIs is crucial for the long-term survival of glioma patients. However, manual segmentation is time-consuming, requiring expert inter…
MINDSETS: Multi-omics Integration with Neuroimaging for Dementia Subtyping and Effective Temporal Study
Salma Hassan, Dawlat Akaila, Maryam Arjemandi +2
In the complex realm of cognitive disorders, Alzheimer's disease (AD) and vascular dementia (VaD) are the two most prevalent dementia types, presenting entangled symptoms yet requi…
Leveraging Self-Supervised Learning for Fetal Cardiac Planes Classification using Ultrasound Scan Videos
Joseph Geo Benjamin, Mothilal Asokan, Amna Alhosani +5
Self-supervised learning (SSL) methods are popular since they can address situations with limited annotated data by directly utilising the underlying data distribution. However, th…
On Evaluating Adversarial Robustness of Volumetric Medical Segmentation Models
Hashmat Shadab Malik, Numan Saeed, Asif Hanif +4
Volumetric medical segmentation models have achieved significant success on organ and tumor-based segmentation tasks in recent years. However, their vulnerability to adversarial at…
On Enhancing Brain Tumor Segmentation Across Diverse Populations with Convolutional Neural Networks
Fadillah Maani, Anees Ur Rehman Hashmi, Numan Saeed +1
Brain tumor segmentation is a fundamental step in assessing a patient's cancer progression. However, manual segmentation demands significant expert time to identify tumors in 3D mu…
PEMMA: Parameter-Efficient Multi-Modal Adaptation for Medical Image Segmentation
Nada Saadi, Numan Saeed, Mohammad Yaqub +1
Imaging modalities such as Computed Tomography (CT) and Positron Emission Tomography (PET) are key in cancer detection, inspiring Deep Neural Networks (DNN) models that merge these…
EDUE: Expert Disagreement-Guided One-Pass Uncertainty Estimation for Medical Image Segmentation
Kudaibergen Abutalip, Numan Saeed, Ikboljon Sobirov +3
Deploying deep learning (DL) models in medical applications relies on predictive performance and other critical factors, such as conveying trustworthy predictive uncertainty. Uncer…
Advanced Tumor Segmentation in Medical Imaging: An Ensemble Approach for BraTS 2023 Adult Glioma and Pediatric Tumor Tasks
Fadillah Maani, Anees Ur Rehman Hashmi, Mariam Aljuboory +3
Automated segmentation proves to be a valuable tool in precisely detecting tumors within medical images. The accurate identification and segmentation of tumor types hold paramount…
XReal: Realistic Anatomy and Pathology-Aware X-ray Generation via Controllable Diffusion Model
Anees Ur Rehman Hashmi, Ibrahim Almakky, Mohammad Areeb Qazi +4
Large-scale generative models have demonstrated impressive capabilities in producing visually compelling images, with increasing applications in medical imaging. However, they cont…
Multi-Task Learning Approach for Unified Biometric Estimation from Fetal Ultrasound Anomaly Scans
Mohammad Areeb Qazi, Mohammed Talha Alam, Ibrahim Almakky +3
Precise estimation of fetal biometry parameters from ultrasound images is vital for evaluating fetal growth, monitoring health, and identifying potential complications reliably. Ho…