10 citations · 30 across the 75 of their papers we have counts for
27 papers · 1 filter
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…
Rethinking Weight-Averaged Model-merging
Hu Wang, Congbo Ma, Ibrahim Almakky +3
Model merging, particularly through weight averaging, has shown surprising effectiveness in saving computations and improving model performance without any additional training. How…
FedSECA: Sign Election and Coordinate-wise Aggregation of Gradients for Byzantine Tolerant Federated Learning
Joseph Geo Benjamin, Mothilal Asokan, Mohammad Yaqub +1
One of the most common defense strategies against Byzantine clients in federated learning (FL) is to employ a robust aggregator mechanism that makes the training more resilient. Wh…
Y-CA-Net: A Convolutional Attention Based Network for Volumetric Medical Image Segmentation
Muhammad Hamza Sharif, Muzammal Naseer, Mohammad Yaqub +2
Recent attention-based volumetric segmentation (VS) methods have achieved remarkable performance in the medical domain which focuses on modeling long-range dependencies. However, f…
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…
Can language-guided unsupervised adaptation improve medical image classification using unpaired images and texts?
Umaima Rahman, Raza Imam, Mohammad Yaqub +2
In medical image classification, supervised learning is challenging due to the scarcity of labeled medical images. To address this, we leverage the visual-textual alignment within…
A Federated Learning-Friendly Approach for Parameter-Efficient Fine-Tuning of SAM in 3D Segmentation
Mothilal Asokan, Joseph Geo Benjamin, Mohammad Yaqub +1
Adapting foundation models for medical image analysis requires finetuning them on a considerable amount of data because of extreme distribution shifts between natural (source) data…
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…
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…
DynaMMo: Dynamic Model Merging for Efficient Class Incremental Learning for Medical Images
Mohammad Areeb Qazi, Ibrahim Almakky, Anees Ur Rehman Hashmi +2
Continual learning, the ability to acquire knowledge from new data while retaining previously learned information, is a fundamental challenge in machine learning. Various approache…
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…
Envisioning MedCLIP: A Deep Dive into Explainability for Medical Vision-Language Models
Anees Ur Rehman Hashmi, Dwarikanath Mahapatra, Mohammad Yaqub
Explaining Deep Learning models is becoming increasingly important in the face of daily emerging multimodal models, particularly in safety-critical domains like medical imaging. Ho…
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…
TiBiX: Leveraging Temporal Information for Bidirectional X-ray and Report Generation
Santosh Sanjeev, Fadillah Adamsyah Maani, Arsen Abzhanov +4
With the emergence of vision language models in the medical imaging domain, numerous studies have focused on two dominant research activities: (1) report generation from Chest X-ra…
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…
MedPromptX: Grounded Multimodal Prompting for Chest X-ray Diagnosis
Mai A. Shaaban, Adnan Khan, Mohammad Yaqub
Chest X-ray images are commonly used for predicting acute and chronic cardiopulmonary conditions, but efforts to integrate them with structured clinical data face challenges due to…
FissionFusion: Fast Geometric Generation and Hierarchical Souping for Medical Image Analysis
Santosh Sanjeev, Nuren Zhaksylyk, Ibrahim Almakky +3
The scarcity of well-annotated medical datasets requires leveraging transfer learning from broader datasets like ImageNet or pre-trained models like CLIP. Model soups averages mult…
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…
MedMerge: Merging Models for Effective Transfer Learning to Medical Imaging Tasks
Ibrahim Almakky, Santosh Sanjeev, Anees Ur Rehman Hashmi +3
Transfer learning has become a powerful tool to initialize deep learning models to achieve faster convergence and higher performance. This is especially useful in the medical imagi…
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…