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The accurate segmentation of breast tumours is important for the diagnosis and treatment of breast cancer. When using the classic U‐Net, Attention‐UNet, and UNet++ to segment the tumour, there are problems of oversegmentation, incorrect segmentation, and poor edge continuity. In this paper, an effective tumour segmentation method, EfficientUNet, is proposed. EfficientUNet adopts a step‐by‐step enhancement method, combining ResNet18, a channel attention mechanism and deep supervision. ResNet18, as the encoder of the whole network, solves the problem of gradient disappearanceand improves the feature extraction ability of the model. The channel attention module makes the model more accurate in tumour edge processing. The deep supervision technology accelerates the model training and provides the convergence direction for the model. In addition, it is found that when adjusting the size of the image, the method of image filling before clipping (or zooming) is more conducive to model learning than the direct interpolation method. And a comparative experiment wasperformed on dataset B. Compared to U‐Net, Attention‐UNet and UNet++, EfficientUNet has the highest performance. Finally, the ablation experiment also indicated the effectiveness of each module in EfficientUNet. [ABSTRACT FROM AUTHOR]

Skin cancer is common and deadly, needs to be detected and treated properly. Deep learning algorithms like UNet have shown potential results in medical imaging. Such approaches still struggle to capture fine‐grained details and scale differences in skin lesions‐based occlusions' appearance, size etc. This research proposes a redesign UNet, the Multi‐Scale Pyramid Attention Network (MSPAN), to improve skin cancer lesion segmentation. The input data is processed at numerous scales with varied receptive fields. This enhances the network's ability to identify lesion locations by capturing local and global context. Attention approaches also help the network to suppress noise by focusing on informative features. We have evaluated MSPAN model on the publicly available ISIC2018 benchmark dataset for skin lesion segmentation. The method surpasses traditional UNet and other current methods in accuracy and effectiveness. The model also has a post‐processing to estimate lesion area for fast inference, making it suitable for extensive screening. Redesigned UNet with the Multi‐Scale Pyramid Attention Network improves skin cancer lesion segmentation. The model's ability to collect fine‐grained information and handle occlusions allows for more accurate skin cancer diagnosis and treatment. The MSPAN design can improve computer‐aided diagnosis systems and help dermatologists make precise clinical decisions. [ABSTRACT FROM AUTHOR]

The fusion and utilization of multi‐scale deep and shallow features are of great significance in liver tumour segmentation. This study proposes a dual encoding DDS‐UNet liver tumour segmentation method based on multi‐scale deep and shallow feature fusion, aiming to fully achieve the fusion and utilization of deep and shallow features and achieve accurate segmentation. The proposed method mainly consists of residual convolution module fusion residual convolution (FRC), dual encoding end fusion module dual encoding end fusion (DEF), and skip connection fusion module jump connection fusion module (JCF). In the residual convolution module, a layer by layer fused residual convolution is used instead of traditional convolution to achieve better training. In the dual encoding end fusion module, multi‐scale feature fusion at the end provides more comprehensive contextual information, solves the loss of spatial geometric information. The skip connection fusion module reduces the interference of invalid features by changing the weights of spatial attention and channel attention on important features. The Dice coefficient, average intersection to union ratio, accuracy, recall, and accuracy indicators tested on the LiTS dataset were 90.37%, 90.16%, 93.78%, 94.91%, and 98.84%, respectively, which are superior to many advanced liver tumor segmentation methods. [ABSTRACT FROM AUTHOR]