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Found 11 papers, 3 papers with code
Deep Learning for Classification of Inflammatory Bowel Disease Activity in Whole Slide Images of Colonic Histopathology
In this study, we developed a deep learning model to classify activity grades in hematoxylin and eosin-stained whole slide images (WSIs) from patients with IBD, offering a robust approach for general pathologists.
A Novel Framework for the Automated Characterization of Gram-Stained Blood Culture Slides Using a Large-Scale Vision Transformer
This study introduces a new framework for the artificial intelligence-assisted characterization of Gram-stained whole-slide images (WSIs).
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.
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.
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.
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.
Multi-Ontology Refined Embeddings (MORE): A Hybrid Multi-Ontology and Corpus-based Semantic Representation for Biomedical Concepts
For the ontology-based part, we use the Medical Subject Headings (MeSH) ontology and three state-of-the-art ontology-based similarity measures.
Automatic Post-Stroke Lesion Segmentation on MR Images using 3D Residual Convolutional Neural Network
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-weighted MRI scans for chronic stroke patients.
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.
Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks
It achieved a kappa score of 0. 525 and an agreement of 66. 6% with three pathologists for classifying the predominant patterns, slightly higher than the inter-pathologist kappa score of 0. 485 and agreement of 62. 7% on this test set.
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
