Search Results for author:
Found 31 papers, 6 papers with code
Improving Neural Models for Radiology Report Retrieval with Lexicon-based Automated Annotation
However, these methods still lack semantic understanding of the underlying clinical conditions as well as ruled out findings, resulting in poor precision during retrieval.
Image-Based Soil Organic Carbon Remote Sensing from Satellite Images with Fourier Neural Operator and Structural Similarity
To study the use of CNNs on SOC remote sensing, here we propose the FNO-DenseNet based on the Fourier neural operator (FNO).
Multimodal Machine Learning in Image-Based and Clinical Biomedicine: Survey and Prospects
Machine learning (ML) applications in medical artificial intelligence (AI) systems have shifted from traditional and statistical methods to increasing application of deep learning models.
HartleyMHA: Self-Attention in Frequency Domain for Resolution-Robust and Parameter-Efficient 3D Image Segmentation
With the introduction of Transformers, different attention-based models have been proposed for image segmentation with promising results.
FNOSeg3D: Resolution-Robust 3D Image Segmentation with Fourier Neural Operator
Due to the computational complexity of 3D medical image segmentation, training with downsampled images is a common remedy for out-of-memory errors in deep learning.
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 for an outcome may be captured across multiple modalities ranging from clinical to imaging and genomic data.
Medical visual question answering using joint self-supervised learning
Visual Question Answering (VQA) becomes one of the most active research problems in the medical imaging domain.
Unsupervised ensemble-based phenotyping helps enhance the discoverability of genes related to heart morphology
Here, we propose a new framework for gene discovery entitled Unsupervised Phenotype Ensembles (UPE).
Towards Automatic Prediction of Outcome in Treatment of Cerebral Aneurysms
Residual flow into the sac after the intervention is a failure that could be due to the use of an undersized device, or to vascular anatomy and clinical condition of the patient.
Fusing Modalities by Multiplexed Graph Neural Networks for Outcome Prediction in Tuberculosis
All these nuances make simple methods of early, late, or intermediate fusion of features inadequate for outcome prediction.
Spatially-Preserving Flattening for Location-Aware Classification of Findings in Chest X-Rays
In this paper, we present a new spatially preserving deep learning network that preserves location and shape information through auto-encoding of feature maps during flattening.
Addressing Deep Learning Model Uncertainty in Long-Range Climate Forecasting with Late Fusion
Global warming leads to the increase in frequency and intensity of climate extremes that cause tremendous loss of lives and property.
Multi-modality fusion using canonical correlation analysis methods: Application in breast cancer survival prediction from histology and genomics
We propose a two-stage prediction pipeline using pCCA embeddings generated with deflation for latent variable prediction by combining all the above.
Multiview and Multiclass Image Segmentation using Deep Learning in Fetal Echocardiography
Congenital heart disease (CHD) is the most common congenital abnormality associated with birth defects in the United States.
Multimodal fusion using sparse CCA for breast cancer survival prediction
Effective understanding of a disease such as cancer requires fusing multiple sources of information captured across physical scales by multimodal data.
Extracting and Learning Fine-Grained Labels from Chest Radiographs
Chest radiographs are the most common diagnostic exam in emergency rooms and intensive care units today.
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 new role is emerging for machines as intelligent assistants to radiologists in their clinical workflows.
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 anomalies in medical imaging.
Chest X-ray Report Generation through Fine-Grained Label Learning
Obtaining automated preliminary read reports for common exams such as chest X-rays will expedite clinical workflows and improve operational efficiencies in hospitals.
Neural Network Segmentation of Cell Ultrastructure Using Incomplete Annotation
The Pancreatic beta cell is an important target in diabetes research.
Multimodal fusion of imaging and genomics for lung cancer recurrence prediction
Lung cancer has a high rate of recurrence in early-stage patients.
Automated Detection and Type Classification of Central Venous Catheters in Chest X-Rays
Central venous catheters (CVCs) are commonly used in critical care settings for monitoring body functions and administering medications.
Boosting the rule-out accuracy of deep disease detection using class weight modifiers
We experiment with two different deep neural network architectures and show that the proposed method results in a large improvement in the performance of the classifiers, specially on negated findings.
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 hospitals.
Identifying disease-free chest X-ray images with deep transfer learning
In this work, we report a deep neural network trained for classifying CXRs with the goal of identifying a large number of normal (disease-free) images without risking the discharge of sick patients.
Age prediction using a large chest X-ray dataset
Age prediction based on appearances of different anatomies in medical images has been clinically explored for many decades.
Classification of radiology reports by modality and anatomy: A comparative study
Data labeling is currently a time-consuming task that often requires expert knowledge.
Bimodal network architectures for automatic generation of image annotation from text
The source of big data is typically large image collections and clinical reports recorded for these images.
3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes
In this paper, we propose a network architecture and the corresponding loss function which improve segmentation of very small structures.
Building medical image classifiers with very limited data using segmentation networks
Deep learning has shown promising results in medical image analysis, however, the lack of very large annotated datasets confines its full potential.
Building Disease Detection Algorithms with Very Small Numbers of Positive Samples
We train a discriminative segmentation model only on normal images to provide a source of knowledge to be transferred to a disease detection classifier.
