4 citations · 6 across the 5 of their papers we have counts for
7 papers
Adaptive Compression-based Lifelong Learning
Shivangi Srivastava, Maxim Berman, Matthew B. Blaschko +1
The problem of a deep learning model losing performance on a previously learned task when fine-tuned to a new one is a phenomenon known as Catastrophic forgetting. There are two ma…
An Ensemble Deep Learning Based Approach for Red Lesion Detection in Fundus Images
José Ignacio Orlando, Elena Prokofyeva, Mariana del Fresno +1
Diabetic retinopathy is one of the leading causes of preventable blindness in the world. Its earliest sign are red lesions, a general term that groups both microaneurysms and hemor…
Intraoperative margin assessment of human breast tissue in optical coherence tomography images using deep neural networks
Amal Rannen Triki, Matthew B. Blaschko, Yoon Mo Jung +4
Objective: In this work, we perform margin assessment of human breast tissue from optical coherence tomography (OCT) images using deep neural networks (DNNs). This work simulates a…
An Efficient Decomposition Framework for Discriminative Segmentation with Supermodular Losses
Jiaqian Yu, Matthew B. Blaschko
Several supermodular losses have been shown to improve the perceptual quality of image segmentation in a discriminative framework such as a structured output support vector machine…
A Convex Surrogate Operator for General Non-Modular Loss Functions
Jiaqian Yu, Matthew Blaschko
Empirical risk minimization frequently employs convex surrogates to underlying discrete loss functions in order to achieve computational tractability during optimization. However,…
A U-statistic Approach to Hypothesis Testing for Structure Discovery in Undirected Graphical Models
Wacha Bounliphone, Matthew Blaschko
Structure discovery in graphical models is the determination of the topology of a graph that encodes conditional independence properties of the joint distribution of all variables…