UNet and its latest extensions like TransUNet have been the leading medical image segmentation methods in recent years. However, these networks cannot be effectively adopted for rapid image ...

Customized implementation of the U-Net in PyTorch for Kaggle's Carvana Image Masking Challenge from high definition images. This model was trained from scratch with 5k images and scored a Dice ...

A library of open datasets for data analytics/machine learning compiled by HackerNoon. The two most widely-used open-source machine learning frameworks for training and building deep learning models ...

Deep convolutional neural network (CNN) greatly promotes the automatic segmentation of medical images. However, due to the inherent properties of convolution operations, CNN usually cannot establish ...

Due to the unique structure of coconuts, their cultivation heavily relies on manual experience, making it difficult to accurately and timely observe their internal characteristics. This limitation ...

Abstract: Modern remote sensing technology has developed rapidly in recent years. The high-resolution remote sensing images brought by new technologies have good application prospects in military and ...

Abstract: The fault diagnosis of electrical equipment plays a vital role in the safe operation of the power system. The task of electrical thermal image semantic segmentation is to segment all ...

To avoid the problems of relative overlap and low signal-to-noise ratio (SNR) of segmented three-dimensional (3D) multimodal medical images, which limit the effect of medical image diagnosis, a 3D ...