18 citations · 31 across the 5 of their papers we have counts for
5 papers
Evolutionary Computation in Action: Feature Selection for Deep Embedding Spaces of Gigapixel Pathology Images
Azam Asilian Bidgoli, Shahryar Rahnamayan, Taher Dehkharghanian +2
One of the main obstacles of adopting digital pathology is the challenge of efficient processing of hyperdimensional digitized biopsy samples, called whole slide images (WSIs). Exp…
Gram Barcodes for Histopathology Tissue Texture Retrieval
Shalev Lifshitz, Abtin Riasatian, H. R. Tizhoosh
Recent advances in digital pathology have led to the need for Histopathology Image Retrieval (HIR) systems that search through databases of biopsy images to find similar cases to a…
Searching for Pneumothorax in X-Ray Images Using Autoencoded Deep Features
Antonio Sze-To, Abtin Riasatian, Hamid R. Tizhoosh
Fast diagnosis and treatment of pneumothorax, a collapsed or dropped lung, is crucial to avoid fatalities. Pneumothorax is typically detected on a chest X-ray image through visual…
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…
A Comparative Study of U-Net Topologies for Background Removal in Histopathology Images
Abtin Riasatian, Maral Rasoolijaberi, Morteza Babaei +1
During the last decade, the digitization of pathology has gained considerable momentum. Digital pathology offers many advantages including more efficient workflows, easier collabor…