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SeUNet-Trans: A Simple yet Effective UNet-Transformer Model for Medical Image Segmentation
Automated medical image segmentation is becoming increasingly crucial to modern clinical practice, driven by the growing demand for precise diagnosis, the push towards personalized treatment plans, and the advancements in machine learning algorithms, especially the incorporation of deep learning methods.
A Comprehensive Review of Generative AI in Healthcare
This review paper aims to offer a thorough overview of the generative AI applications in healthcare, focusing on transformers and diffusion models.
Adaptive Reorganization of Neural Pathways for Continual Learning with Spiking Neural Networks
In this paper, we propose a brain-inspired continual learning algorithm with adaptive reorganization of neural pathways, which employs Self-Organizing Regulation networks to reorganize the single and limited Spiking Neural Network (SOR-SNN) into rich sparse neural pathways to efficiently cope with incremental tasks.
Deep Learning-based Prediction of Stress and Strain Maps in Arterial Walls for Improved Cardiovascular Risk Assessment
The trained U-Net models can accurately predict von Mises stress and strain fields, with structural similarity index scores (SSIM) of 0. 854 and 0. 830 and mean squared errors of 0. 017 and 0. 018 for stress and strain, respectively, on a reserved test set.
An Integrated Inverse Space Sparse Representation Framework for Tumor Classification
Extensive experiments on six public microarray gene expression datasets show the integrated ISSRC-based tumor classification framework is superior to classical and state-of-the-art methods.
