17 citations · 36 across the 41 of their papers we have counts for
14 papers · 1 filter
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…
FIVA: Federated Inverse Variance Averaging for Universal CT Segmentation with Uncertainty Estimation
Asim Ukaye, Numan Saeed, Karthik Nandakumar
Different CT segmentation datasets are typically obtained from different scanners under different capture settings and often provide segmentation labels for a limited and often dis…
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…
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…
Improving Stain Invariance of CNNs for Segmentation by Fusing Channel Attention and Domain-Adversarial Training
Kudaibergen Abutalip, Numan Saeed, Mustaqeem Khan +1
Variability in staining protocols, such as different slide preparation techniques, chemicals, and scanner configurations, can result in a diverse set of whole slide images (WSIs).…
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…
TMSS: An End-to-End Transformer-based Multimodal Network for Segmentation and Survival Prediction
Numan Saeed, Ikboljon Sobirov, Roba Al Majzoub +1
When oncologists estimate cancer patient survival, they rely on multimodal data. Even though some multimodal deep learning methods have been proposed in the literature, the majorit…
Super Images -- A New 2D Perspective on 3D Medical Imaging Analysis
Ikboljon Sobirov, Numan Saeed, Mohammad Yaqub
In medical imaging analysis, deep learning has shown promising results. We frequently rely on volumetric data to segment medical images, necessitating the use of 3D architectures,…
Is it Possible to Predict MGMT Promoter Methylation from Brain Tumor MRI Scans using Deep Learning Models?
Numan Saeed, Shahad Hardan, Kudaibergen Abutalip +1
Glioblastoma is a common brain malignancy that tends to occur in older adults and is almost always lethal. The effectiveness of chemotherapy, being the standard treatment for most…
An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Data
Numan Saeed, Roba Al Majzoub, Ikboljon Sobirov +1
Accurate prognosis of a tumor can help doctors provide a proper course of treatment and, therefore, save the lives of many. Traditional machine learning algorithms have been eminen…