The purpose of medical image segmentation is to identify target organs, tissues or lesion
areas at the pixel level to help doctors evaluate and prevent diseases. Therefore, the model …

Medical image segmentation and classification are fundamental tasks in computer-aided
diagnosis, where accurate segmentation plays a key role in identifying disease-related …

Segmentation is an important task in a wide range of computer vision applications, including
medical image analysis. Recent years have seen an increase in the complexity of medical …

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 …

Deep learning architecture with convolutional neural network achieves outstanding success
in the field of computer vision. Where U-Net has made a great breakthrough in biomedical …

Deep learning shows excellent performance in medical image segmentation. However, a
pooling operation in its encoding stage leads to feature loss and the ability of multi-scale …

Accurate and automated segmentation of medical images plays a crucial role in diagnostic
evaluation and treatment planning. In recent years, hybrid models have gained considerable …

Medical image segmentation plays a crucial role in computer-aided diagnosis. However,
due to the large variability of medical images, accurate segmentation is a highly challenging …

In recent years, deep learning-based methods have achieved remarkable progress in
medical image processing, like polyp segmentation in colonoscopy images and skin lesion …

The major challenges for medical image segmentation tasks are complex backgrounds and
fuzzy boundaries. In order to reduce their negative impacts on medical image segmentation …