35 citations · 93 across the 12 of their papers we have counts for
12 papers
ALFA -- Leveraging All Levels of Feature Abstraction for Enhancing the Generalization of Histopathology Image Classification Across Unseen Hospitals
Milad Sikaroudi, Maryam Hosseini, Shahryar Rahnamayan +1
We propose an exhaustive methodology that leverages all levels of feature abstraction, targeting an enhancement in the generalizability of image classification to unobserved hospit…
Comments on 'Fast and scalable search of whole-slide images via self-supervised deep learning'
Milad Sikaroudi, Mehdi Afshari, Abubakr Shafique +2
Chen et al. [Chen2022] recently published the article 'Fast and scalable search of whole-slide images via self-supervised deep learning' in Nature Biomedical Engineering. The autho…
Cluster Based Secure Multi-Party Computation in Federated Learning for Histopathology Images
S. Maryam Hosseini, Milad Sikaroudi, Morteza Babaei +1
Federated learning (FL) is a decentralized method enabling hospitals to collaboratively learn a model without sharing private patient data for training. In FL, participant hospital…
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…
Batch-Incremental Triplet Sampling for Training Triplet Networks Using Bayesian Updating Theorem
Milad Sikaroudi, Benyamin Ghojogh, Fakhri Karray +2
Variants of Triplet networks are robust entities for learning a discriminative embedding subspace. There exist different triplet mining approaches for selecting the most suitable t…
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…
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…
Weighted Fisher Discriminant Analysis in the Input and Feature Spaces
Benyamin Ghojogh, Milad Sikaroudi, H. R. Tizhoosh +2
Fisher Discriminant Analysis (FDA) is a subspace learning method which minimizes and maximizes the intra- and inter-class scatters of data, respectively. Although, in FDA, all the…