39 citations · 91 across the 33 of their papers we have counts for
7 papers · 2 filters
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…
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,…
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…
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…
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…