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Found 14 papers, 3 papers with code
Metrics reloaded: Pitfalls and recommendations for image analysis validation
The field of automatic biomedical image analysis crucially depends on robust and meaningful performance metrics for algorithm validation.
Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases
With these constraints in mind, we propose FedCy, a federated semi-supervised learning (FSSL) method that combines FL and self-supervised learning to exploit a decentralized dataset of both labeled and unlabeled videos, thereby improving performance on the task of surgical phase recognition.
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.
Common Limitations of Image Processing Metrics: A Picture Story
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation.
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.
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.
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.
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.
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 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.
Color Space Transformation Network
Given a large dataset of colored images the color space transformation module tries to learn color space transformations that increase overall classification accuracy.
