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Found 3 papers, 3 papers with code
RadFormer: Transformers with Global-Local Attention for Interpretable and Accurate Gallbladder Cancer Detection
We propose a novel deep neural network architecture to learn interpretable representation for medical image analysis.
Unsupervised Contrastive Learning of Image Representations from Ultrasound Videos with Hard Negative Mining
We further validate the generalizability of our method on a publicly available lung US image dataset of COVID-19 pathologies and show an improvement of 1. 5% compared to SOTA.
Surpassing the Human Accuracy: Detecting Gallbladder Cancer from USG Images with Curriculum Learning
However, USG images are challenging to analyze due to low image quality, noise, and varying viewpoints due to the handheld nature of the sensor.
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Gallbladder Cancer Detection
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