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Found 6 papers, 4 papers with code
Weakly Supervised Contrastive Learning for Chest X-Ray Report Generation
Radiology report generation aims at generating descriptive text from radiology images automatically, which may present an opportunity to improve radiology reporting and interpretation.
Knee menisci segmentation and relaxometry of 3D ultrashort echo time (UTE) cones MR imaging using attention U-Net with transfer learning
Next, the T1, T1$\rho$, T2* relaxations, and ROI areas were determined for the manual and automatic segmentations, then compared. The models developed using ROIs provided by two radiologists achieved high Dice scores of 0. 860 and 0. 833, while the radiologists' manual segmentations achieved a Dice score of 0. 820.
Perceptual Compressive Sensing
In detail, we employ perceptual loss, defined on feature level, to enhance the structure information of the recovered images.
Full Image Recover for Block-Based Compressive Sensing
In measurement part, the input image is adaptively measured block by block to acquire a group of measurements.
Fully Convolutional Measurement Network for Compressive Sensing Image Reconstruction
Recently, deep learning methods have made a significant improvement in compressive sensing image reconstruction task.
Adaptive Measurement Network for CS Image Reconstruction
In this paper, we propose an adaptive measurement network in which measurement is obtained by learning.
