65 citations · 121 across the 7 of their papers we have counts for
8 papers
The intersection of video capsule endoscopy and artificial intelligence: addressing unique challenges using machine learning
Shan Guleria, Benjamin Schwartz, Yash Sharma +12
Introduction: Technical burdens and time-intensive review processes limit the practical utility of video capsule endoscopy (VCE). Artificial intelligence (AI) is poised to address…
HistoTransfer: Understanding Transfer Learning for Histopathology
Yash Sharma, Lubaina Ehsan, Sana Syed +1
Advancement in digital pathology and artificial intelligence has enabled deep learning-based computer vision techniques for automated disease diagnosis and prognosis. However, WSIs…
Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classification
Yash Sharma, Aman Shrivastava, Lubaina Ehsan +3
In recent years, the availability of digitized Whole Slide Images (WSIs) has enabled the use of deep learning-based computer vision techniques for automated disease diagnosis. Howe…
Advancing Eosinophilic Esophagitis Diagnosis and Phenotype Assessment with Deep Learning Computer Vision
William Adorno, Alexis Catalano, Lubaina Ehsan +5
Eosinophilic Esophagitis (EoE) is an inflammatory esophageal disease which is increasing in prevalence. The diagnostic gold-standard involves manual review of a patient's biopsy ti…
HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach
Kamran Kowsari, Rasoul Sali, Lubaina Ehsan +7
Image classification is central to the big data revolution in medicine. Improved information processing methods for diagnosis and classification of digital medical images have show…
Hierarchical Deep Convolutional Neural Networks for Multi-category Diagnosis of Gastrointestinal Disorders on Histopathological Images
Rasoul Sali, Sodiq Adewole, Lubaina Ehsan +8
Deep convolutional neural networks(CNNs) have been successful for a wide range of computer vision tasks, including image classification. A specific area of the application lies in…