13 citations · 23 across the 5 of their papers we have counts for
7 papers
Hospital-Agnostic Image Representation Learning in Digital Pathology
Milad Sikaroudi, Shahryar Rahnamayan, H. R. Tizhoosh
Whole Slide Images (WSIs) in digital pathology are used to diagnose cancer subtypes. The difference in procedures to acquire WSIs at various trial sites gives rise to variability i…
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…
Magnification Generalization for Histopathology Image Embedding
Milad Sikaroudi, Benyamin Ghojogh, Fakhri Karray +2
Histopathology image embedding is an active research area in computer vision. Most of the embedding models exclusively concentrate on a specific magnification level. However, a use…
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…
Fisher Discriminant Triplet and Contrastive Losses for Training Siamese Networks
Benyamin Ghojogh, Milad Sikaroudi, Sobhan Shafiei +3
Siamese neural network is a very powerful architecture for both feature extraction and metric learning. It usually consists of several networks that share weights. The Siamese conc…