198 citations · 226 across the 13 of their papers we have counts for
13 papers
Cross-Modal Contrastive Learning from Histopathology and CT for Automated Renal Cell Carcinoma Grading
Amit Das, Tanmay Shukla, Naofumi Tomita +9
Background: Clear cell renal cell carcinoma (ccRCC) exhibits substantial clinical heterogeneity, and accurate grade assessment is essential for risk stratification and treatment pl…
Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images
Amit Das, Naofumi Tomita, Kyle J. Syme +6
Hematoxylin and Eosin (H&E) staining is a cornerstone of pathological analysis, offering reliable visualization of cellular morphology and tissue architecture for cancer diagnosis,…
Deep Learning for Classification of Inflammatory Bowel Disease Activity in Whole Slide Images of Colonic Histopathology
Amit Das, Tanmay Shukla, Naofumi Tomita +4
Grading inflammatory bowel disease (IBD) activity using standardized histopathological scoring systems remains challenging due to resource constraints and inter-observer variabilit…
A Novel Framework for the Automated Characterization of Gram-Stained Blood Culture Slides Using a Large-Scale Vision Transformer
Jack McMahon, Naofumi Tomita, Elizabeth S. Tatishev +5
This study introduces a new framework for the artificial intelligence-assisted characterization of Gram-stained whole-slide images (WSIs). As a test for the diagnosis of bloodstrea…
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…
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…
Multi-Ontology Refined Embeddings (MORE): A Hybrid Multi-Ontology and Corpus-based Semantic Representation for Biomedical Concepts
Steven Jiang, Weiyi Wu, Naofumi Tomita +2
Objective: Currently, a major limitation for natural language processing (NLP) analyses in clinical applications is that a concept can be referenced in various forms across differe…
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…
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…
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…
Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks
Jason W. Wei, Laura J. Tafe, Yevgeniy A. Linnik +3
Classification of histologic patterns in lung adenocarcinoma is critical for determining tumor grade and treatment for patients. However, this task is often challenging due to the…
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…