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Found 26 papers, 13 papers with code
Automatic Report Generation for Histopathology images using pre-trained Vision Transformers and BERT
Deep learning for histopathology has been successfully used for disease classification, image segmentation and more.
Caption Generation
Histopathological Image Classification
+4
Automatic Report Generation for Histopathology images using pre-trained Vision Transformers
In this work, we show that using an existing pre-trained Vision Transformer in a two-step process of first using it to encode 4096x4096 sized patches of the Whole Slide Image (WSI) and then using it as the encoder and an LSTM decoder for report generation, we can build a fairly performant and portable report generation mechanism that takes into account the whole of the high resolution image, instead of just the patches.
Label-efficient Contrastive Learning-based model for nuclei detection and classification in 3D Cardiovascular Immunofluorescent Images
Previous methods use Maximum Intensity Projection (MIP) to convert immunofluorescent images with multiple slices to 2D images, which can cause signals from different z-stacks to falsely appear associated with each other.
Analyzing historical diagnosis code data from NIH N3C and RECOVER Programs using deep learning to determine risk factors for Long Covid
Using our deep learning approach, we are able to predict if a patient is suffering from Long COVID from a temporally ordered list of diagnosis codes up to 45 days post the first COVID positive test or diagnosis for each patient, with an accuracy of 70. 48\%.
Weakly Supervised Deep Instance Nuclei Detection using Points Annotation in 3D Cardiovascular Immunofluorescent Images
In this study, we used a weakly supervised learning approach to train the HoVer-Net segmentation model using point annotations to detect nuclei in fluorescent images.
MaNi: Maximizing Mutual Information for Nuclei Cross-Domain Unsupervised Segmentation
Nuclei vary substantially in structure and appearances across different cancer types, leading to a drop in performance of deep learning models when trained on one cancer type and tested on another.
Encoding Cardiopulmonary Exercise Testing Time Series as Images for Classification using Convolutional Neural Network
Exercise testing has been available for more than a half-century and is a remarkably versatile tool for diagnostic and prognostic information of patients for a range of diseases, especially cardiovascular and pulmonary.
Toward a Knowledge Discovery Framework for Data Science Job Market in the United States
The growth of the data science field requires better tools to understand such a fast-paced growing domain.
HistoTransfer: Understanding Transfer Learning for Histopathology
In this work, we compare the performance of features extracted from networks trained on ImageNet and histopathology data.
Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classification
We regularized the clustering mechanism by introducing a KL-divergence loss between the attention weights of patches in a cluster and the uniform distribution.
Advancing Eosinophilic Esophagitis Diagnosis and Phenotype Assessment with Deep Learning Computer Vision
A U-Net model and post-processing system are applied to generate eosinophil-based statistics that can diagnose EoE as well as describe disease severity and progression.
Hierarchical Deep Convolutional Neural Networks for Multi-category Diagnosis of Gastrointestinal Disorders on Histopathological Images
The results indicate that the hierarchical model can achieve better results than the flat model for multi-category diagnosis of GI disorders using histopathological images.
Gender Detection on Social Networks using Ensemble Deep Learning
Analyzing the ever-increasing volume of posts on social media sites such as Facebook and Twitter requires improved information processing methods for profiling authorship.
Dialogue-Based Simulation For Cultural Awareness Training
Based on training on our dataset, the models score the trainees' responses for cultural awareness in the Chinese culture.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Women in ISIS Propaganda: A Natural Language Processing Analysis of Topics and Emotions in a Comparison with Mainstream Religious Group
Online propaganda is central to the recruitment strategies of extremist groups and in recent years these efforts have increasingly extended to women.
CeliacNet: Celiac Disease Severity Diagnosis on Duodenal Histopathological Images Using Deep Residual Networks
These results demonstrate the diagnostic power of the proposed model for CD severity classification using histological images.
Self-Attentive Adversarial Stain Normalization
We propose a Self-Attentive Adversarial Stain Normalization (SAASN) approach for the normalization of multiple stain appearances to a common domain.
Deep Learning for Visual Recognition of Environmental Enteropathy and Celiac Disease
Physicians use biopsies to distinguish between different but histologically similar enteropathies.
Text Classification Algorithms: A Survey
In this paper, a brief overview of text classification algorithms is discussed.
Diagnosis of Celiac Disease and Environmental Enteropathy on Biopsy Images Using Color Balancing on Convolutional Neural Networks
We evaluated the performance of our proposed model using a large cohort containing 1000 biopsy images.
From Videos to URLs: A Multi-Browser Guide To Extract User's Behavior with Optical Character Recognition
This method could provide another way to collect users' activities during an online session given that the session recorder collected the data.
Analysis of Railway Accidents' Narratives Using Deep Learning
These reports contain a variety of fixed field entries including primary cause of the accidents (a coded variable with 389 values) as well as a narrative field which is a short text description of the accident.
An Improvement of Data Classification Using Random Multimodel Deep Learning (RMDL)
In this paper, we describe RMDL model and compare the results for image and text classification as well as face recognition.
RMDL: Random Multimodel Deep Learning for Classification
This paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble, deep learning approach for classification.
HDLTex: Hierarchical Deep Learning for Text Classification
This is because along with this growth in the number of documents has come an increase in the number of categories.
Ranked #1 on
Document Classification
on WOS-46985
PrivLogit: Efficient Privacy-preserving Logistic Regression by Tailoring Numerical Optimizers
Leveraging this new method, we propose two new secure protocols for conducting logistic regression in a privacy-preserving and distributed manner.
