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Found 7 papers, 1 papers with code
Improving Model's Interpretability and Reliability using Biomarkers
Accurate and interpretable diagnostic models are crucial in the safety-critical field of medicine.
Learning Generic Lung Ultrasound Biomarkers for Decoupling Feature Extraction from Downstream Tasks
We propose to decouple feature learning from downstream lung ultrasound tasks by introducing an auxiliary pre-task of visual biomarker classification.
Dense Pixel-Labeling for Reverse-Transfer and Diagnostic Learning on Lung Ultrasound for COVID-19 and Pneumonia Detection
Our segmentation-based models perform better classification when using pretrained segmentation weights, with the dense-label pretrained U-Net performing the best.
The Role of Pleura and Adipose in Lung Ultrasound AI
In this paper, we study the significance of the pleura and adipose tissue in lung ultrasound AI analysis.
Weakly Supervised Contrastive Learning for Better Severity Scoring of Lung Ultrasound
Due to this, a large amount of lung ultrasound scans have been made available which can be used for AI based diagnosis and analysis.
Exploiting Class Similarity for Machine Learning with Confidence Labels and Projective Loss Functions
We view this type of clustering as valuable information and exploit it with our novel projective loss functions.
W-Net: Dense Semantic Segmentation of Subcutaneous Tissue in Ultrasound Images by Expanding U-Net to Incorporate Ultrasound RF Waveform Data
To the best of our knowledge, this is also the first deep-learning or CNN approach for segmentation that analyses ultrasound raw RF data along with the gray image.
