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Found 16 papers, 3 papers with code
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.
MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation
Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving provider and patient experience.
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.
Just-Enough Interaction Approach to Knee MRI Segmentation: Data from the Osteoarthritis Initiative
State-of-the-art automated segmentation algorithms are not 100\% accurate especially when segmenting difficult to interpret datasets like those with severe osteoarthritis (OA).
Automated Segmentation of Knee MRI Using Hierarchical Classifiers and Just Enough Interaction Based Learning: Data from Osteoarthritis Initiative
We present a fully automated learning-based approach for segmenting knee cartilage in the presence of osteoarthritis (OA).
Learning-Based Cost Functions for 3D and 4D Multi-Surface Multi-Object Segmentation of Knee MRI: Data from the Osteoarthritis Initiative
4D LOGISMOS validation on 108 MRIs from baseline and 12 month follow-up scans of 54 patients showed a significant reduction in segmentation errors (\emph{p}$<$0. 01) compared to 3D.
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.
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.
Distill-to-Label: Weakly Supervised Instance Labeling Using Knowledge Distillation
Weakly supervised instance labeling using only image-level labels, in lieu of expensive fine-grained pixel annotations, is crucial in several applications including medical image analysis.
Self-Training with Improved Regularization for Sample-Efficient Chest X-Ray Classification
Automated diagnostic assistants in healthcare necessitate accurate AI models that can be trained with limited labeled data, can cope with severe class imbalances and can support simultaneous prediction of multiple disease conditions.
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.
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.
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.
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.
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.
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.
