17 citations · 36 across the 41 of their papers we have counts for
11 papers · 1 filter
SurvCORN: Survival Analysis with Conditional Ordinal Ranking Neural Network
Muhammad Ridzuan, Numan Saeed, Fadillah Adamsyah Maani +2
Survival analysis plays a crucial role in estimating the likelihood of future events for patients by modeling time-to-event data, particularly in healthcare settings where predicti…
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…
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…
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…
SurvRNC: Learning Ordered Representations for Survival Prediction using Rank-N-Contrast
Numan Saeed, Muhammad Ridzuan, Fadillah Adamsyah Maani +3
Predicting the likelihood of survival is of paramount importance for individuals diagnosed with cancer as it provides invaluable information regarding prognosis at an early stage.…
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…
HuLP: Human-in-the-Loop for Prognosis
Muhammad Ridzuan, Mai Kassem, Numan Saeed +2
This paper introduces HuLP, a Human-in-the-Loop for Prognosis model designed to enhance the reliability and interpretability of prognostic models in clinical contexts, especially w…
CoReEcho: Continuous Representation Learning for 2D+time Echocardiography Analysis
Fadillah Adamsyah Maani, Numan Saeed, Aleksandr Matsun +1
Deep learning (DL) models have been advancing automatic medical image analysis on various modalities, including echocardiography, by offering a comprehensive end-to-end training pi…
ConDiSR: Contrastive Disentanglement and Style Regularization for Single Domain Generalization
Aleksandr Matsun, Numan Saeed, Fadillah Adamsyah Maani +1
Medical data often exhibits distribution shifts, which cause test-time performance degradation for deep learning models trained using standard supervised learning pipelines. This c…