21 citations · 45 across the 5 of their papers we have counts for
5 papers
Learning to Predict RNA Sequence Expressions from Whole Slide Images with Applications for Search and Classification
Amir Safarpoor, Jason D. Hipp, H. R. Tizhoosh
Deep learning methods are widely applied in digital pathology to address clinical challenges such as prognosis and diagnosis. As one of the most recent applications, deep models ha…
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…
Offline versus Online Triplet Mining based on Extreme Distances of Histopathology Patches
Milad Sikaroudi, Benyamin Ghojogh, Amir Safarpoor +3
We analyze the effect of offline and online triplet mining for colorectal cancer (CRC) histopathology dataset containing 100,000 patches. We consider the extreme, i.e., farthest an…
Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study
Milad Sikaroudi, Amir Safarpoor, Benyamin Ghojogh +3
As many algorithms depend on a suitable representation of data, learning unique features is considered a crucial task. Although supervised techniques using deep neural networks hav…
Pan-Cancer Diagnostic Consensus Through Searching Archival Histopathology Images Using Artificial Intelligence
Shivam Kalra, H. R. Tizhoosh, Sultaan Shah +8
The emergence of digital pathology has opened new horizons for histopathology and cytology. Artificial-intelligence algorithms are able to operate on digitized slides to assist pat…