Search Results for author:
Found 9 papers, 3 papers with code
Attention-Based Deep Neural Networks for Detection of Cancerous and Precancerous Esophagus Tissue on Histopathological Slides
Deep learning-based methods, such as the sliding window approach for cropped-image classification and heuristic aggregation for whole-slide inference, for analyzing histological patterns in high-resolution microscopy images have shown promising results.
Ranked #1 on
Medical Object Detection
on Barrett’s Esophagus
Automated detection of celiac disease on duodenal biopsy slides: a deep learning approach
In this study, we trained a deep learning model to detect celiac disease on duodenal biopsy images.
Deep neural networks for automated classification of colorectal polyps on histopathology slides: A multi-institutional evaluation
An accurate and automated algorithm for the classification of colorectal polyps on digitized histopathology slides could benefit clinicians and patients.
Generative Image Translation for Data Augmentation in Colorectal Histopathology Images
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 that can lead to colorectal cancer if left untreated.
Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator
The system is built on the pa-rameter server architecture and aims to speed up the model training via utilizing a cluster of servers in case of large volume of training data.
Difficulty Translation in Histopathology Images
Our model comprises a scorer, which provides an output confidence to measure the difficulty of images, and an image translator, which learns to translate images from easy-to-classify to hard-to-classify using a training set defined by the scorer.
Learn like a Pathologist: Curriculum Learning by Annotator Agreement for Histopathology Image Classification
Based on the nature of histopathology images, a range of difficulty inherently exists among examples, and, since medical datasets are often labeled by multiple annotators, annotator agreement can be used as a natural proxy for the difficulty of a given example.
Development and Evaluation of a Deep Neural Network for Histologic Classification of Renal Cell Carcinoma on Biopsy and Surgical Resection Slides
In this study, we developed a deep neural network model that can accurately classify digitized surgical resection slides and biopsy slides into five related classes: clear cell RCC, papillary RCC, chromophobe RCC, renal oncocytoma, and normal.
A Petri Dish for Histopathology Image Analysis
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 resected specimens traditionally manually examined under a microscope by pathologists.
