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07/13/2023
MaxCorrMGNN: A Multi-Graph Neural Network Framework for Generalized Multimodal Fusion of Medical Data for Outcome Prediction
With the emergence of multimodal electronic health records, the evidence...
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02/25/2023
Medical visual question answering using joint self-supervised learning
Visual Question Answering (VQA) becomes one of the most active research ...
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01/07/2023
Unsupervised ensemble-based phenotyping helps enhance the discoverability of genes related to heart morphology
Recent genome-wide association studies (GWAS) have been successful in id...
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10/25/2022
Fusing Modalities by Multiplexed Graph Neural Networks for Outcome Prediction in Tuberculosis
In a complex disease such as tuberculosis, the evidence for the disease ...
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04/19/2022
Spatially-Preserving Flattening for Location-Aware Classification of Findings in Chest X-Rays
Chest X-rays have become the focus of vigorous deep learning research in...
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12/10/2021
Addressing Deep Learning Model Uncertainty in Long-Range Climate Forecasting with Late Fusion
Global warming leads to the increase in frequency and intensity of clima...
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11/27/2021
Multi-modality fusion using canonical correlation analysis methods: Application in breast cancer survival prediction from histology and genomics
The availability of multi-modality datasets provides a unique opportunit...
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03/23/2021
Multiview and Multiclass Image Segmentation using Deep Learning in Fetal Echocardiography
Congenital heart disease (CHD) is the most common congenital abnormality...
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03/09/2021
Multimodal fusion using sparse CCA for breast cancer survival prediction
Effective understanding of a disease such as cancer requires fusing mult...
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11/18/2020
Extracting and Learning Fine-Grained Labels from Chest Radiographs
Chest radiographs are the most common diagnostic exam in emergency rooms...
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09/13/2020
Receptivity of an AI Cognitive Assistant by the Radiology Community: A Report on Data Collected at RSNA
Due to advances in machine learning and artificial intelligence (AI), a ...
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08/02/2020
Looking in the Right place for Anomalies: Explainable AI through Automatic Location Learning
Deep learning has now become the de facto approach to the recognition of...
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07/27/2020
Chest X-ray Report Generation through Fine-Grained Label Learning
Obtaining automated preliminary read reports for common exams such as ch...
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04/20/2020
Neural Network Segmentation of Cell Ultrastructure Using Incomplete Annotation
The Pancreatic beta cell is an important target in diabetes research. Fo...
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02/05/2020
Multimodal fusion of imaging and genomics for lung cancer recurrence prediction
Lung cancer has a high rate of recurrence in early-stage patients. Predi...
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07/02/2019
Automated Detection and Type Classification of Central Venous Catheters in Chest X-Rays
Central venous catheters (CVCs) are commonly used in critical care setti...
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06/21/2019
Boosting the rule-out accuracy of deep disease detection using class weight modifiers
In many screening applications, the primary goal of a radiologist or ass...
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06/21/2019
Building a Benchmark Dataset and Classifiers for Sentence-Level Findings in AP Chest X-rays
Chest X-rays are the most common diagnostic exams in emergency rooms and...
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04/02/2019
Identifying disease-free chest X-ray images with deep transfer learning
Chest X-rays (CXRs) are among the most commonly used medical image modal...
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03/09/2019
Age prediction using a large chest X-ray dataset
Age prediction based on appearances of different anatomies in medical im...
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12/27/2018
Classification of radiology reports by modality and anatomy: A comparative study
Data labeling is currently a time-consuming task that often requires exp...
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09/05/2018
Bimodal network architectures for automatic generation of image annotation from text
Medical image analysis practitioners have embraced big data methodologie...
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08/31/2018
3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes
With the introduction of fully convolutional neural networks, deep learn...
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08/15/2018
Building medical image classifiers with very limited data using segmentation networks
Deep learning has shown promising results in medical image analysis, how...
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05/07/2018
