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Medical image segmentation has garnered significant research attention in the neural network community as a fundamental requirement for developing intelligent medical assistant systems. A series of UNet-like networks with an encoder-decoder architecture have achieved remarkable success in medical image segmentation. Among these networks, UNet2+ (UNet++) and UNet3+ (UNet+++) have introduced redesigned skip connections, dense skip connections, and full-scale skip connections, respectively, surpassing the performance of the original UNet. However, UNet2+ lacks comprehensive information obtained from the entire scale, which hampers its ability to learn organ placement and boundaries. Similarly, due to the limited number of neurons in its structure, UNet3+ fails to effectively segment small objects when trained with a small number of samples. In this study, we propose UNet_sharp (UNet#), a novel network topology named after the "#" symbol, which combines dense skip connections and full-scale skip connections. In the decoder sub-network, UNet# can effectively integrate feature maps of different scales and capture fine-grained features and coarse-grained semantics from the entire scale. This approach enhances the understanding of organ and lesion positions and enables accurate boundary segmentation. We employ deep supervision for model pruning to accelerate testing and enable mobile device deployment. Additionally, we construct two classification-guided modules to reduce false positives and improve segmentation accuracy. Compared to current UNet-like networks, our proposed method achieves the highest Intersection over Union (IoU) values ((92.67±0.96)%, (92.38±1.29)%, (95.36±1.22)%, (74.01±2.03)%) and F1 scores ((91.64±1.86)%, (95.70±2.16)%, (97.34±2.76)%, (84.77±2.65)%) on the semantic segmentation tasks of nuclei, brain tumors, liver, and lung nodules, respectively. The experimental results demonstrate that the reconstructed skip connections in UNet successfully incorporate multi-scale contextual semantic information. Compared to most state-of-the-art medical image segmentation models, our proposed method more accurately locates organs and lesions and precisely segments boundaries. • Proposing an innovative medical image segmentation model named UNet# that effectively integrates different scale semantics of feature maps. • Adding deep supervision to capture coarse-grained and fine-grained semantic information from the full-scale feature maps. • Incorporating deep supervision into the first layer guides model pruning, reducing model parameters and speeding up segmentation. • Designing two classification-guided modules for separate output branches to achieve higher precision segmentation of organs and lesions. • Conducting extensive trials on multiple datasets to validate the consistent improvement of the proposed method in medical image segmentation. [ABSTRACT FROM AUTHOR]

• Improved cost-effective U-type deep learning model for medical image segmentation. • Dual-branch encoder enhance the extraction of semantic information from input images. • Mutations facilitates more flexible and comprehensive feature fusion. • Generalizable model with fewer parameters, lower computational complexity. Medical image segmentation is a critical task in early disease detection and diagnosis. In recent years, numerous variants of U-Net and Transformer-based models have demonstrated success in medical image segmentation. But these models still have certain limitations, particularly regarding the extraction of semantic information and high computational complexity. In order to tackle these challenges, we introduce a cutting-edge model called DualA-Net, which incorporates dual-branch encoder, multi-scale skip connections and Adaptive Receptive Field Selection Decoder(ARFSD). These innovations we proposed enable the model to intelligently adapt to and focus on relevant areas, ensuring its adaptability and thus improving the accuracy and efficiency of the segmentation process. To assess the performance of DualA-Net, its generalization capability was evaluated on five datasets of different segmentation tasks. The experimental results showed that the DualA-Net model performed the best on these datasets. Moreover, it minimized the parameter count and computational complexity. These findings provide evidence supporting the versatility and effectiveness of DualA-Net in medical image segmentation. Codes are available at https://github.com/Ziii1/DualA-Net. [ABSTRACT FROM AUTHOR]

• Developed a residual dense network to reconstruct magnetic resonance images super-resolution. • Designed the multi-residual block. • The proposed super-resolution method has good performance than the state-of-the-art methods. High-resolution magnetic resonance images (MRI) help experts to localize lesions and diagnose diseases, but it is difficult to obtain high-resolution MRI. Furthermore, image super-resolution technology based on deep learning can effectively improve image resolution. In this work, we propose a medical magnetic resonance (MR) image super-resolution reconstruction method based on residual dense network (MRDN). Firstly, we input the convolutional features of the shallow layer into the residual dense block to obtain global and local features. Secondly, each layer in the residual dense block is directly connected to the previous layer to achieve reuse of features. Finally, we use sub-pixel convolution layer for upsampling and super-resolution reconstruction to get a clear high-resolution image. For the 2 ×, 3 ×, and 4 × enlargement, we propose the MRDN method shows the superiority over the state-of-the-art methods on the Set5, Set14, and Urban100 benchmark datasets, extensive benchmark experiment and analysis show that the superiority of our MRDN algorithm in terms of the peak signal-to-noise ratio (PSNR) and structural similarity index indicators (SSIM). Quantitative experiments are conducted on three public datasets: Set5, Set14 and Urban10, evaluate with commonly used evaluation metrics, and the experimental results show that the method in this paper is more effective. In addition, we reconstruct the public MR datasets, and the reconstructed high-resolution MR image has a clear structure and rich texture details. [ABSTRACT FROM AUTHOR]