Search Results for author:
Found 21 papers, 5 papers with code
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.
CheXRelNet: An Anatomy-Aware Model for Tracking Longitudinal Relationships between Chest X-Rays
Despite the progress in utilizing deep learning to automate chest radiograph interpretation and disease diagnosis tasks, change between sequential Chest X-rays (CXRs) has received limited attention.
3D Segmentation with Fully Trainable Gabor Kernels and Pearson's Correlation Coefficient
For existing loss functions for multi-class image segmentation, there is usually a tradeoff among accuracy, robustness to hyperparameters, and manual weight selections for combining different losses.
Basis Scaling and Double Pruning for Efficient Inference in Network-Based Transfer Learning
Network-based transfer learning allows the reuse of deep learning features with limited data, but the resulting models can be unnecessarily large.
Chest ImaGenome Dataset for Clinical Reasoning
Despite the progress in automatic detection of radiologic findings from chest X-ray (CXR) images in recent years, a quantitative evaluation of the explainability of these models is hampered by the lack of locally labeled datasets for different findings.
AnaXNet: Anatomy Aware Multi-label Finding Classification in Chest X-ray
Radiologists usually observe anatomical regions of chest X-ray images as well as the overall image before making a decision.
Channel Scaling: A Scale-and-Select Approach for Transfer Learning
By imposing L1 regularization and thresholding on the scaling weights, this framework iteratively removes unnecessary feature channels from a pre-trained model.
Statistical learning and cross-validation for point processes
This paper presents the first general (supervised) statistical learning framework for point processes in general spaces.
Creation and Validation of a Chest X-Ray Dataset with Eye-tracking and Report Dictation for AI Development
We report deep learning experiments that utilize the attention maps produced by eye gaze dataset to show the potential utility of this data.
Learning Invariant Feature Representation to Improve Generalization across Chest X-ray Datasets
Through pneumonia-classification experiments on multi-source chest X-ray datasets, we show that this algorithm helps in improving classification accuracy on a new source of X-ray dataset.
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.
SegNAS3D: Network Architecture Search with Derivative-Free Global Optimization for 3D Image Segmentation
Deep learning has largely reduced the need for manual feature selection in image segmentation.
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.
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.
