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Found 37 papers, 9 papers with code
A Benchmark for Long-Form Medical Question Answering
There is a lack of benchmarks for evaluating large language models (LLMs) in long-form medical question answering (QA).
ImpScore: A Learnable Metric For Quantifying The Implicitness Level of Language
Handling implicit language is essential for natural language processing systems to achieve precise text understanding and facilitate natural interactions with users.
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.
Improving Colorectal Cancer Screening and Risk Assessment through Predictive Modeling on Medical Images and Records
The evolution of digital pathology and recent advancements in deep learning provide a unique opportunity to investigate the added benefits of including the additional medical record information and automatic processing of pathology slides using computer vision techniques in the calculation of future CRC risk.
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).
MentalManip: A Dataset For Fine-grained Analysis of Mental Manipulation in Conversations
We anticipate that ${\rm M{\small ental}M{\small anip}}$ will stimulate further research, leading to progress in both understanding and mitigating the impact of mental manipulation in conversations.
Prediction of Breast Cancer Recurrence Risk Using a Multi-Model Approach Integrating Whole Slide Imaging and Clinicopathologic Features
The aim of this study was to develop a multi-model approach integrating the analysis of whole slide images and clinicopathologic data to predict their associated breast cancer recurrence risks and categorize these patients into two risk groups according to the predicted score: low and high risk.
Vision Transformer-Based Deep Learning for Histologic Classification of Endometrial Cancer
Endometrial cancer, the fourth most common cancer in females in the United States, with the lifetime risk for developing this disease is approximately 2. 8% in women.
Proto-lm: A Prototypical Network-Based Framework for Built-in Interpretability in Large Language Models
Large Language Models (LLMs) have significantly advanced the field of Natural Language Processing (NLP), but their lack of interpretability has been a major concern.
Improving Representation Learning for Histopathologic Images with Cluster Constraints
This framework aims for transferable representation learning and semantically meaningful clustering by synergizing invariance loss and clustering loss in WSI analysis.
Masked Pre-Training of Transformers for Histology Image Analysis
However, due to the large number of model parameters and limited labeled data, applying transformer models to WSIs remains challenging.
HistoPerm: A Permutation-Based View Generation Approach for Improving Histopathologic Feature Representation Learning
These findings suggest that HistoPerm can be a valuable tool for improving representation learning of histopathology features when access to labeled data is limited and can lead to whole-slide classification results that are comparable to or superior to fully-supervised methods.
Interpretation Quality Score for Measuring the Quality of interpretability methods
Machine learning (ML) models have been applied to a wide range of natural language processing (NLP) tasks in recent years.
Towards Interpretable Deep Reinforcement Learning Models via Inverse Reinforcement Learning
Artificial intelligence, particularly through recent advancements in deep learning, has achieved exceptional performances in many tasks in fields such as natural language processing and computer vision.
Calibrating Histopathology Image Classifiers using Label Smoothing
Moreover, we find that using model confidence as a proxy for annotator agreement also improves calibration and accuracy, suggesting that datasets without multiple annotators can still benefit from our proposed label smoothing methods via our proposed confidence-aware label smoothing methods.
MHAttnSurv: Multi-Head Attention for Survival Prediction Using Whole-Slide Pathology Images
In pathology, whole-slide images (WSI) based survival prediction has attracted increasing interest.
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.
Resolution-Based Distillation for Efficient Histology Image Classification
Our results show that a combination of KD and self-supervision allows the student model to approach, and in some cases, surpass the classification accuracy of the teacher, while being much more efficient.
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.
Sensitivity and Specificity Evaluation of Deep Learning Models for Detection of Pneumoperitoneum on Chest Radiographs
State-of-the-art deep learning models (ResNet101, InceptionV3, DenseNet161, and ResNeXt101) were trained on a subset of this dataset, and the automated classification performance was evaluated on the rest of the dataset by measuring the AUC, sensitivity, and specificity for each model.
Deep Learning in Diabetic Foot Ulcers Detection: A Comprehensive Evaluation
DFUC2020 provided participants with a comprehensive dataset consisting of 2, 000 images for training and 2, 000 images for testing.
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.
A Refined Deep Learning Architecture for Diabetic Foot Ulcers Detection
Diabetic Foot Ulcers (DFU) that affect the lower extremities are a major complication of diabetes.
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.
Self-Supervised Contextual Language Representation of Radiology Reports to Improve the Identification of Communication Urgency
We pre-trained the BERT model on a large unlabeled corpus of radiology reports and used the resulting contextual representations in a final text classifier for communication urgency.
Artificial Intelligence-Based Image Classification for Diagnosis of Skin Cancer: Challenges and Opportunities
Recently, there has been great interest in developing Artificial Intelligence (AI) enabled computer-aided diagnostics solutions for the diagnosis of skin cancer.
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.
Predicting colorectal polyp recurrence using time-to-event analysis of medical records
Identifying patient characteristics that influence the rate of colorectal polyp recurrence can provide important insights into which patients are at higher risk for recurrence.
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.
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.
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.
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
Deep Learning Methods and Applications for Region of Interest Detection in Dermoscopic Images
In addition, we developed an automated natural data-augmentation method from ROI detection to produce augmented copies of dermoscopic images, as a pre-processing step in the segmentation of skin lesions to further improve the performance of the current state-of-the-art deep learning algorithm.
Multi-class Semantic Segmentation of Skin Lesions via Fully Convolutional Networks
Melanoma is clinically difficult to distinguish from common benign skin lesions, particularly melanocytic naevus and seborrhoeic keratosis.
Deep-Learning for Classification of Colorectal Polyps on Whole-Slide Images
In this work, we built an automatic image-understanding method that can accurately classify different types of colorectal polyps in whole-slide histology images to help pathologists with histopathological characterization and diagnosis of colorectal polyps.
