Medical image segmentation's accuracy is crucial for clinical analysis and diagnosis.
Despite progress with U-Net-inspired models, they often underuse multi-scale convolutional …

Medical image segmentation's accuracy is crucial for clinical analysis and diagnosis.
Despite progress with U-Net-inspired models, they often underuse multi-scale encoding …

Background The use of U-Net and its variations has led to significant advancements in
medical image segmentation. However, the encoder-decoder structures of these models …

In recent years, U‐Net and its variants have gained widespread use in medical image
segmentation. One key aspect of U‐Net's design is the skip connection, facilitating the …

In recent years, encoder-decoder networks have focused on expanding receptive fields and
incorporating multi-scale context to capture global features for objects of varying sizes …

Deep learning has demonstrated exceptional efficacy in medical image segmentation.
However, the pursuit of superior performance has often driven researchers to devise …

Precise segmentation is vital for successful diagnosis and treatment planning. Medical
image segmentation has demonstrated remarkable advances with the introduction of deep …

Accurate segmentation of medical images is crucial for disease diagnosis and
understanding disease changes. Deep learning methods, utilizing encoder-decoder …

A variety of convolutional neural network (CNN) based methods for medical image
segmentation have achieved outstanding performance, however, inherently suffered from a …

Medical image segmentation is an important and complex task in clinical practices, but the
widely used U-Net usually cannot achieve satisfactory performances in some clinical …