Fusing Brilliance: Evaluating the Encoder-Decoder Hybrids With CNN and Swin Transformer for Medical Segmentation [PDF]
Seunghyuk Lee, Songkuk Kim
openalex +1 more source
ST-CFI: Swin Transformer with convolutional feature interactions for identifying plant diseases. [PDF]
Yu S, Xie L, Dai L.
europepmc +1 more source
Swin Transformer Based Recognition for Hydraulic Fracturing Microseismic Signals from Coal Seam Roof with Ultra Large Mining Height. [PDF]
Wang P, Feng Y, Sun X, Cheng X.
europepmc +1 more source
Innovative patient-specific delivered-dose prediction for volumetric modulated arc therapy using lightweight Swin-Transformer. [PDF]
Zhou Y, Gong C, Jian J, Zhang Y.
europepmc +1 more source
Medical image segmentation by combining feature enhancement Swin Transformer and UperNet. [PDF]
Zhang L, Yin X, Liu X, Liu Z.
europepmc +1 more source
Low resolution remote sensing object detection with fine grained enhancement and swin transformer. [PDF]
Xu Z, Wang X, Huang K, Chen R.
europepmc +1 more source
Multi-phase deep learning framework with Multiscale Adaptive Swin Transformer and embedding attention for precision lung nodule detection and classification. [PDF]
M D, B RAP.
europepmc +1 more source

