18 citations · 25 across the 14 of their papers we have counts for
17 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…
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…
Ranking Loss and Sequestering Learning for Reducing Image Search Bias in Histopathology
Pooria Mazaheri, Azam Asilian Bidgoli, Shahryar Rahnamayan +1
Recently, deep learning has started to play an essential role in healthcare applications, including image search in digital pathology. Despite the recent progress in computer visio…
Variable Functioning and Its Application to Large Scale Steel Frame Design Optimization
Amir H Gandomi, Kalyanmoy Deb, Ronald C Averill +2
To solve complex real-world problems, heuristics and concept-based approaches can be used in order to incorporate information into the problem. In this study, a concept-based appro…
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…
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…