49 citations · 66 across the 7 of their papers we have counts for
10 papers
MHAttnSurv: Multi-Head Attention for Survival Prediction Using Whole-Slide Pathology Images
Shuai Jiang, Arief A. Suriawinata, Saeed Hassanpour
In pathology, whole-slide images (WSI) based survival prediction has attracted increasing interest. However, given the large size of WSIs and the lack of pathologist annotations, e…
Resolution-Based Distillation for Efficient Histology Image Classification
Joseph DiPalma, Arief A. Suriawinata, Laura J. Tafe +2
Developing deep learning models to analyze histology images has been computationally challenging, as the massive size of the images causes excessive strain on all parts of the comp…
A Petri Dish for Histopathology Image Analysis
Jerry Wei, Arief Suriawinata, Bing Ren +9
With the rise of deep learning, there has been increased interest in using neural networks for histopathology image analysis, a field that investigates the properties of biopsy or…
Learn like a Pathologist: Curriculum Learning by Annotator Agreement for Histopathology Image Classification
Jerry Wei, Arief Suriawinata, Bing Ren +10
Applying curriculum learning requires both a range of difficulty in data and a method for determining the difficulty of examples. In many tasks, however, satisfying these requireme…
Difficulty Translation in Histopathology Images
Jerry Wei, Arief Suriawinata, Xiaoying Liu +5
The unique nature of histopathology images opens the door to domain-specific formulations of image translation models. We propose a difficulty translation model that modifies color…
Predicting colorectal polyp recurrence using time-to-event analysis of medical records
Lia X. Harrington, Jason W. Wei, Arief A. Suriawinata +2
Identifying patient characteristics that influence the rate of colorectal polyp recurrence can provide important insights into which patients are at higher risk for recurrence. We…