Traditionally, to improve the segmentation performance of models, most approaches prefer to use more complex modules. This is not suitable for the medical field, especially for mobile medical devices, where computationally loaded models are not suitable for real clinical environments due to computational resource constraints. Recently, state-space models, represented by Mamba, have become a strong competitor to traditional convolutional neural networks and transformers. In this paper, we deeply explore the key elements of parameter influence in Mamba and propose an UltraLight Vision Mamba UNet (UltraLight VM-UNet) based on this. Specifically, we propose a method for processing features in parallel Vision Mamba, named the PVM Layer, which achieves competitive performance with the lowest computational complexity while keeping the overall number of processing channels constant. We conducted segmentation experiments on three public datasets of skin lesions and showed that UltraLight VM-UNet exhibits competitive performance with only 0.049M parameters and 0.060 GFLOPs.

The Segment Anything Model (SAM) has shown exceptional versatility in segmentation tasks across various natural image scenarios. However, its application to medical image segmentation poses significant challenges due to the intricate anatomical details and domain-specific characteristics inherent in medical images. To address these challenges, we propose a novel VMamba adapter framework that integrates a lightweight, trainable Visual Mamba (VMamba) branch with the pre-trained SAM ViT encoder. The VMamba adapter accurately captures multi-scale contextual correlations, integrates global and local information, and reduces ambiguities arising from local features only. Specifically, we propose a novel cross-branch attention (CBA) mechanism to facilitate effective interaction between the SAM and VMamba branches. This mechanism enables the model to learn and adapt more efficiently to the nuances of medical images, extracting rich, complementary features that enhance its representational capacity. Beyond architectural enhancements, we streamline the segmentation workflow by eliminating the need for prompt-driven input mechanisms. This results in an autonomous prediction model that reduces manual input requirements and improves operational efficiency. In addition, our method introduces only minimal additional trainable parameters, offering an efficient solution for medical image segmentation. Extensive evaluations of four medical image datasets demonstrate that our VMamba adapter framework achieves state-of-the-art performance. Specifically, on the ACDC dataset with limited training data, our method achieves an average Dice coefficient improvement of 0.18 and reduces the Hausdorff distance by 20.38 mm compared to the AutoSAM.

The ongoing outbreak of COVID-19 has been announced by the World Health Organization as a worldwide public health emergency. The aim of this study was to distinguish between severe and non-severe patients in early diagnosis. The results showed that the mortality of COVID-19 patients increased accompanied by age. Host factors CRP, IL-1β, hs-CRP, IL-8, and IL-6 levels in severe pneumonia patients were higher than in non-severe patients. CD3, CD8, and CD45 counts were decreased in COVID-19 patients. The results of this study suggest that the K-values of CD45 might be useful in distinguishing between severe and non-severe cases. The cut-off value for CD45 was -94.33. The K-values for CD45 in non-severe case were above the cut-off values, indicating a 100% prediction success rate for severe and non-severe cases following SARS-CoV-2 infection. The results confirmed that immune system dysfunction is a potential cause of mortality following COVID-19 infection, particularly for the elderly. CD45 deficiency dysfunction the naïve and memory T lymphocytes which may affects the long-term effectiveness of COVID-19 vaccines. K-values of CD45 might be useful in distinguishing between severe and non-severe cases in the early infection. May be CD45 could increase the diagnostic sensitivity.

To explore the application value of convolutional neural network combined with residual attention mechanism and Xception model for automatic classification of benign and malignant gastric ulcer lesions in common digestive endoscopy images under the condition of insufficient data. For the problems of uneven illumination and low resolution of endoscopic images, the original image is preprocessed by Sobel operator, etc. The algorithm model is implemented by Pytorch, and the preprocessed image is used as input data. The model is based on convolutional neural network for automatic classification and diagnosis of benign and malignant gastric ulcer lesions in small number of digestive endoscopy images. The accuracy, F1 score, sensitivity, specificity and precision of the Xception model improved by the residual attention module for the diagnosis of benign and malignant gastric ulcer lesions were 81.411%, 81.815%, 83.751%, 76.827% and 80.111%, respectively. The superposition of residual attention modules can effectively improve the feature learning ability of the model. The pretreatment of digestive endoscopy can remove the interference information on the digestive endoscopic image data extracted from the database, which is beneficial to the training of the model. The residual attention mechanism can effectively improve the classification effect of Xception convolutional neural network on benign and malignant lesions of gastric ulcer on common digestive endoscopic images.

Ultra-wide field (UWF) retinal imaging can improve the detection rate of retinal hemorrhage as compared with conventional fundus images. However, hemorrhages in UWF retinal images can also become smaller and more widely distributed, which can be time consuming and labor intensive. With the development of computer technology, automatic segmentation techniques can assist physicians in diagnosis. However, the lack of publicly available UWF retinal hemorrhage segmentation datasets has limited the development of automatic hemorrhage segmentation techniques in UWF retinal images. We present a large-scale high-quality UWF retinal hemorrhage segmentation dataset, named UWF-RHS Dataset, for public use. To the best of our knowledge, we are the first team to make the UWF retinal hemorrhage segmentation dataset publicly available. In addition, we propose a multi-scale attention subtraction network (MASNet) for UWF retinal hemorrhage segmentation. Specifically, highly focused lesion features are extracted by using the proposed multi-scale attention subtraction (MAS) module at the progress of the skip-connection. Several comparative experiments and ablation experiments were performed at the UWF-RHS Dataset, and all experiments state that our proposed method is effective in diagnosing retinal hemorrhages with state-of-the-art results. The proposed UWF-RHS dataset and MASNet will greatly facilitate the development of automated segmentation techniques for UWF retinal hemorrhages. Links to the UWF-RHS dataset and the MASNet model code are available from https://github.com/wurenkai/UWF-RHS-Dataset-and-MASNet .