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Found 5 papers, 1 papers with code
Label-efficient Multi-organ Segmentation Method with Diffusion Model
In this study, we present a label-efficient learning approach using a pre-trained diffusion model for multi-organ segmentation tasks in CT images.
Quantitative perfusion maps using a novelty spatiotemporal convolutional neural network
The results indicate that the network can accurately estimate perfusion parameters, including cerebral blood volume (CBV), cerebral blood flow (CBF), and time to maximum of the residual function (Tmax).
A Comparative Study of Pre-trained CNNs and GRU-Based Attention for Image Caption Generation
Image captioning is a challenging task involving generating a textual description for an image using computer vision and natural language processing techniques.
Bayesian inference for data-efficient, explainable, and safe robotic motion planning: A review
Fifth, we systematically present the hybridization of Bayesian inference and RL which is a promising direction to improve the convergence of RL for better motion planning.
3D Cross-Pseudo Supervision (3D-CPS): A semi-supervised nnU-Net architecture for abdominal organ segmentation
Large curated datasets are necessary, but annotating medical images is a time-consuming, laborious, and expensive process.
