13 citations · 19 across the 6 of their papers we have counts for
6 papers
Monitoring Shortcut Learning using Mutual Information
Mohammed Adnan, Yani Ioannou, Chuan-Yung Tsai +3
The failure of deep neural networks to generalize to out-of-distribution data is a well-known problem and raises concerns about the deployment of trained networks in safety-critica…
Domain-Agnostic Clustering with Self-Distillation
Mohammed Adnan, Yani A. Ioannou, Chuan-Yung Tsai +1
Recent advancements in self-supervised learning have reduced the gap between supervised and unsupervised representation learning. However, most self-supervised and deep clustering…
Pay Attention with Focus: A Novel Learning Scheme for Classification of Whole Slide Images
Shivam Kalra, Mohammed Adnan, Sobhan Hemati +3
Deep learning methods such as convolutional neural networks (CNNs) are difficult to directly utilize to analyze whole slide images (WSIs) due to the large image dimensions. We over…
Fine-Tuning and Training of DenseNet for Histopathology Image Representation Using TCGA Diagnostic Slides
Abtin Riasatian, Morteza Babaie, Danial Maleki +19
Feature vectors provided by pre-trained deep artificial neural networks have become a dominant source for image representation in recent literature. Their contribution to the perfo…
Representation Learning of Histopathology Images using Graph Neural Networks
Mohammed Adnan, Shivam Kalra, Hamid R. Tizhoosh
Representation learning for Whole Slide Images (WSIs) is pivotal in developing image-based systems to achieve higher precision in diagnostic pathology. We propose a two-stage frame…
Learning Permutation Invariant Representations using Memory Networks
Shivam Kalra, Mohammed Adnan, Graham Taylor +1
Many real-world tasks such as classification of digital histopathology images and 3D object detection involve learning from a set of instances. In these cases, only a group of inst…