39 citations · 91 across the 33 of their papers we have counts for
33 papers
Automatic Quality Assessment of First Trimester Crown-Rump-Length Ultrasound Images
Sevim Cengiz, Ibraheem Hamdi, Mohammad Yaqub
Fetal gestational age (GA) is vital clinical information that is estimated during pregnancy in order to assess fetal growth. This is usually performed by measuring the crown-rump-l…
TransResNet: Integrating the Strengths of ViTs and CNNs for High Resolution Medical Image Segmentation via Feature Grafting
Muhammad Hamza Sharif, Dmitry Demidov, Asif Hanif +2
High-resolution images are preferable in medical imaging domain as they significantly improve the diagnostic capability of the underlying method. In particular, high resolution hel…
DGM-DR: Domain Generalization with Mutual Information Regularized Diabetic Retinopathy Classification
Aleksandr Matsun, Dana O. Mohamed, Sharon Chokuwa +2
The domain shift between training and testing data presents a significant challenge for training generalizable deep learning models. As a consequence, the performance of models tra…
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
Karim Lekadir, Aasa Feragen, Abdul Joseph Fofanah +117
Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. I…
PECon: Contrastive Pretraining to Enhance Feature Alignment between CT and EHR Data for Improved Pulmonary Embolism Diagnosis
Santosh Sanjeev, Salwa K. Al Khatib, Mai A. Shaaban +3
Previous deep learning efforts have focused on improving the performance of Pulmonary Embolism(PE) diagnosis from Computed Tomography (CT) scans using Convolutional Neural Networks…
SEDA: Self-Ensembling ViT with Defensive Distillation and Adversarial Training for robust Chest X-rays Classification
Raza Imam, Ibrahim Almakky, Salma Alrashdi +2
Deep Learning methods have recently seen increased adoption in medical imaging applications. However, elevated vulnerabilities have been explored in recent Deep Learning solutions,…
Structurally Different Neural Network Blocks for the Segmentation of Atrial and Aortic Perivascular Adipose Tissue in Multi-centre CT Angiography Scans
Ikboljon Sobirov, Cheng Xie, Muhammad Siddique +26
Since the emergence of convolutional neural networks (CNNs) and, later, vision transformers (ViTs), deep learning architectures have predominantly relied on identical block types w…
Prompt-Based Tuning of Transformer Models for Multi-Center Medical Image Segmentation of Head and Neck Cancer
Numan Saeed, Muhammad Ridzuan, Roba Al Majzoub +1
Medical image segmentation is a vital healthcare endeavor requiring precise and efficient models for appropriate diagnosis and treatment. Vision transformer (ViT)-based segmentatio…
Intuitive Surgical SurgToolLoc and SurgVU Challenges Results: 2022-2025
Aneeq Zia, Max Berniker, Rogerio Garcia Nespolo +153
Robotic assisted (RA) surgery promises to transform surgical intervention. Intuitive Surgical is committed to fostering these changes and the machine learning models and algorithms…
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…
Balancing Privacy and Performance for Private Federated Learning Algorithms
Xiangjian Hou, Sarit Khirirat, Mohammad Yaqub +1
Federated learning (FL) is a distributed machine learning (ML) framework where multiple clients collaborate to train a model without exposing their private data. FL involves cycles…
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Simon Graham, Quoc Dang Vu, Mostafa Jahanifar +86
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovatio…
Weakly Unsupervised Domain Adaptation for Vestibular Schwannoma Segmentation
Shahad Hardan, Hussain Alasmawi, Xiangjian Hou +1
Vestibular schwannoma (VS) is a non-cancerous tumor located next to the ear that can cause hearing loss. Most brain MRI images acquired from patients are contrast-enhanced T1 (ceT1…
FUSQA: Fetal Ultrasound Segmentation Quality Assessment
Sevim Cengiz, Ibrahim Almakky, Mohammad Yaqub
Deep learning models have been effective for various fetal ultrasound segmentation tasks. However, generalization to new unseen data has raised questions about their effectiveness…
Learning Confident Classifiers in the Presence of Label Noise
Asma Ahmed Hashmi, Aigerim Zhumabayeva, Nikita Kotelevskii +4
The success of Deep Neural Network (DNN) models significantly depends on the quality of provided annotations. In medical image segmentation, for example, having multiple expert ann…
On the Importance of Image Encoding in Automated Chest X-Ray Report Generation
Otabek Nazarov, Mohammad Yaqub, Karthik Nandakumar
Chest X-ray is one of the most popular medical imaging modalities due to its accessibility and effectiveness. However, there is a chronic shortage of well-trained radiologists who…
A Radiogenomics Pipeline for Lung Nodules Segmentation and Prediction of EGFR Mutation Status from CT Scans
Ivo Gollini Navarrete, Mohammad Yaqub
Lung cancer is a leading cause of death worldwide. Early-stage detection of lung cancer is essential for a more favorable prognosis. Radiogenomics is an emerging discipline that co…
How to Train Vision Transformer on Small-scale Datasets?
Hanan Gani, Muzammal Naseer, Mohammad Yaqub
Vision Transformer (ViT), a radically different architecture than convolutional neural networks offers multiple advantages including design simplicity, robustness and state-of-the-…
Self-omics: A Self-supervised Learning Framework for Multi-omics Cancer Data
Sayed Hashim, Karthik Nandakumar, Mohammad Yaqub
We have gained access to vast amounts of multi-omics data thanks to Next Generation Sequencing. However, it is challenging to analyse this data due to its high dimensionality and m…
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…
EchoCoTr: Estimation of the Left Ventricular Ejection Fraction from Spatiotemporal Echocardiography
Rand Muhtaseb, Mohammad Yaqub
Learning spatiotemporal features is an important task for efficient video understanding especially in medical images such as echocardiograms. Convolutional neural networks (CNNs) a…
Self-Ensembling Vision Transformer (SEViT) for Robust Medical Image Classification
Faris Almalik, Mohammad Yaqub, Karthik Nandakumar
Vision Transformers (ViT) are competing to replace Convolutional Neural Networks (CNN) for various computer vision tasks in medical imaging such as classification and segmentation.…
GARDNet: Robust Multi-View Network for Glaucoma Classification in Color Fundus Images
Ahmed Al Mahrooqi, Dmitrii Medvedev, Rand Muhtaseb +1
Glaucoma is one of the most severe eye diseases, characterized by rapid progression and leading to irreversible blindness. It is often the case that diagnostics is carried out when…
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,…