49 citations · 92 across the 13 of their papers we have counts for
18 papers
Calibrating Histopathology Image Classifiers using Label Smoothing
Jerry Wei, Lorenzo Torresani, Jason Wei +1
The classification of histopathology images fundamentally differs from traditional image classification tasks because histopathology images naturally exhibit a range of diagnostic…
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…
Development and Evaluation of a Deep Neural Network for Histologic Classification of Renal Cell Carcinoma on Biopsy and Surgical Resection Slides
Mengdan Zhu, Bing Ren, Ryland Richards +3
Renal cell carcinoma (RCC) is the most common renal cancer in adults. The histopathologic classification of RCC is essential for diagnosis, prognosis, and management of patients. R…
Sensitivity and Specificity Evaluation of Deep Learning Models for Detection of Pneumoperitoneum on Chest Radiographs
Manu Goyal, Judith Austin-Strohbehn, Sean J. Sun +4
Background: Deep learning has great potential to assist with detecting and triaging critical findings such as pneumoperitoneum on medical images. To be clinically useful, the perfo…