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Found 4 papers, 3 papers with code
Medical Image Captioning via Generative Pretrained Transformers
The proposed model is tested on two medical datasets, the Open-I, MIMIC-CXR, and the general-purpose MS-COCO.
First U-Net Layers Contain More Domain Specific Information Than The Last Ones
Our results demonstrate that 1) domain-shift may deteriorate the quality even for a simple brain extraction segmentation task (surface Dice Score drops from 0. 85-0. 89 even to 0. 09); 2) fine-tuning of the first layers significantly outperforms fine-tuning of the last layers in almost all supervised domain adaptation setups.
Anomaly Detection in Medical Imaging with Deep Perceptual Autoencoders
To address this problem, we introduce a new powerful method of image anomaly detection.
Perceptual Image Anomaly Detection
We present a novel method for image anomaly detection, where algorithms that use samples drawn from some distribution of "normal" data, aim to detect out-of-distribution (abnormal) samples.
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