we propose a novel module named fine-grained combination of Transformer and CNN(FcTC). FcTC module is composed of dual-path extractor and fusing unit to effectively extract local information and model ...

7 models architectures for binary and multi class segmentation (including legendary Unet) 15 available encoders All encoders have pre-trained weights for faster and better convergence 35% or more ...

Using the Shift Window UNET ... autoencoder. Having tuned these foundation models, we evaluated them across downstream classifier tasks associated with the differential diagnosis of Parkinsonian ...

This is an autoencoder with cylic loss and coding parsing loss for image compression and reconstruction. Network backbone is simple 3-layer fully conv (encoder) and symmetrical for decoder. Finally it ...