7 citations · 12 across the 8 of their papers we have counts for
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Adaptive Input-image Normalization for Solving the Mode Collapse Problem 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…
Assessing Intra-class Diversity and Quality of Synthetically Generated Images in a Biomedical and Non-biomedical Setting
Muhammad Muneeb Saad, Mubashir Husain Rehmani, Ruairi O'Reilly
In biomedical image analysis, data imbalance is common across several imaging modalities. Data augmentation is one of the key solutions in addressing this limitation. Generative Ad…
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…
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…