7 citations · 12 across the 8 of their papers we have counts for
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A Self-attention Guided Multi-scale Gradient GAN for Diversified X-ray Image Synthesis
Muhammad Muneeb Saad, Mubashir Husain Rehmani, Ruairi O'Reilly
Imbalanced image datasets are commonly available in the domain of biomedical image analysis. Biomedical images contain diversified features that are significant in predicting targe…
Evaluating the Quality and Diversity of DCGAN-based Generatively Synthesized Diabetic Retinopathy Imagery
Cristina-Madalina Dragan, Muhammad Muneeb Saad, Mubashir Husain Rehmani +1
Publicly available diabetic retinopathy (DR) datasets are imbalanced, containing limited numbers of images with DR. This imbalance contributes to overfitting when training machine…
Classification of Stress via Ambulatory ECG and GSR Data
Zachary Dair, Muhammad Muneeb Saad, Urja Pawar +2
In healthcare, detecting stress and enabling individuals to monitor their mental health and wellbeing is challenging. Advancements in wearable technology now enable continuous phys…
Addressing the Intra-class Mode Collapse Problem using Adaptive Input Image Normalization in GAN-based X-ray Images
Muhammad Muneeb Saad, Mubashir Husain Rehmani, Ruairi O'Reilly
Biomedical image datasets can be imbalanced due to the rarity of targeted diseases. Generative Adversarial Networks play a key role in addressing this imbalance by enabling the gen…
A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysis
Muhammad Muneeb Saad, Ruairi O'Reilly, Mubashir Husain Rehmani
In biomedical image analysis, the applicability of deep learning methods is directly impacted by the quantity of image data available. This is due to deep learning models requiring…