8 citations · 11 across the 7 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
MGMT promoter methylation status prediction using MRI scans? An extensive experimental evaluation of deep learning models
Numan Saeed, Muhammad Ridzuan, Hussain Alasmawi +2
The number of studies on deep learning for medical diagnosis is expanding, and these systems are often claimed to outperform clinicians. However, only a few systems have shown medi…