198 citations · 351 across the 25 of their papers we have counts for
11 papers · 1 filter
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…
Automatic Post-Stroke Lesion Segmentation on MR Images using 3D Residual Convolutional Neural Network
Naofumi Tomita, Steven Jiang, Matthew E. Maeder +1
In this paper, we demonstrate the feasibility and performance of deep residual neural networks for volumetric segmentation of irreversibly damaged brain tissue lesions on T1-weight…
Artificial Intelligence-Based Image Classification for Diagnosis of Skin Cancer: Challenges and Opportunities
Manu Goyal, Thomas Knackstedt, Shaofeng Yan +1
Recently, there has been great interest in developing Artificial Intelligence (AI) enabled computer-aided diagnostics solutions for the diagnosis of skin cancer. With the increasin…
Generative Image Translation for Data Augmentation in Colorectal Histopathology Images
Jerry Wei, Arief Suriawinata, Louis Vaickus +4
We present an image translation approach to generate augmented data for mitigating data imbalances in a dataset of histopathology images of colorectal polyps, adenomatous tumors th…
Deep neural networks for automated classification of colorectal polyps on histopathology slides: A multi-institutional evaluation
Jason W. Wei, Arief A. Suriawinata, Louis J. Vaickus +10
Histological classification of colorectal polyps plays a critical role in both screening for colorectal cancer and care of affected patients. An accurate and automated algorithm fo…
Attention-Based Deep Neural Networks for Detection of Cancerous and Precancerous Esophagus Tissue on Histopathological Slides
Naofumi Tomita, Behnaz Abdollahi, Jason Wei +3
Deep learning-based methods, such as the sliding window approach for cropped-image classification and heuristic aggregation for whole-slide inference, for analyzing histological pa…