Diffusion probabilistic models (DPMs) have recently demonstrated promising performance in medical image segmentation. However, traditional DPM has difficulty handling the irregular structure of images and the inherent similarity between lesions and surrounding tissues. To overcome these challenges, we propose an innovative architecture, the multiscale Mamba DPM (MsM-DPM), designed to enhance medical image segmentation. Specifically, MsM-DPM introduces a multiscale attention fusion module (MSAFM) in a multiscale denoising UNet (Ms-DU) to capture lesion deformations from multilevel features, thereby enhancing the model's robustness to shape and scale variations. Furthermore, in the segmentation network, a multilayer axial feature module (MLAFM) is used to adaptively aggregate the global context features from the Mamba encoder to enhance the expression of features in the spatial dimension by capturing axial multiscale features. The multilevel global context (MLGC) module is then used to reconstruct skip connections using graph convolutional network inference, and the enhanced features are assigned to each layer in the decoder to capture the contextual relationship of features. Finally, the feature fusion module (FFM) integrates deep features with upsampled features in the decoder, enhancing the network's ability to capture lesion boundary details. Our MsM-DPM effectively encodes the semantic difference between lesions and background to improve the representation of their internal features. Extensive experiments on six datasets, LUNA16, ATM22, COVID-19, Self-collected datasets, Pancreas, and BT-MSD, show that the proposed MsM-DPM outperforms existing segmentation methods. Our code is publicly available at https://github.com/suhuaqiang/deep-learning.

Multiple Myeloma (MM) is a growing global health concern, and early diagnosis is crucial for effective treatment. Efforts are underway to produce digital pathology tools with human-level intelligence that are efficient, scalable, accessible, and cost-effective. Microscopic images have high resolution, where cells are enormous and dense. Therefore, the annotation process is time-consuming and complex for tasks such as segmentation due to pixel-level marking. In this paper, we design an end-to-end weakly supervised myeloma cell segmentation framework based on point annotation. It can achieve accurate cell segmentation comparable to fully supervised methods while reducing the need for manual annotation, greatly shortening annotation time. Experimental results demonstrate that our method achieves 98% of its fully-supervised performance with only 10 annotated random points per instance, and outperforms the fully-supervised Mask RCNN.

Accurate classification of coronary artery plaques can provide effective assistance for the diagnosis of coronary artery disease(CAD). The task of coronary artery plaque classification remains extremely challenging due to the complex anatomical structure and background of coronary arteries. 3D convolution still has limitations in feature modeling, so this study builds a dual branch bridge network based on convolution neural network (CNN) and Transformer framework, and fused the local feature extraction ability of convolution and the global modeling ability of Transformer through the bridge communication module. By using a shift attention (SA) module at the intersection of dual branch information to utilizes minimal computational complexity to fuse feature maps from both branches. The ghost plus (GP) module was aimed at balancing the enormous computational power issues of building rich semantic information and training difficulties in 3D Transformers. The proposed method have demonstrated the effectiveness through a large number of comparative and ablation experiment.

Diffusion probabilistic models (DPM) can generate semantically valuable pixel-level representations and are widely used in medical image segmentation tasks. However, DPM faces challenges when dealing with medical image segmentation problems due to the irregular structure of medical images and the similarity between lesions and their surrounding environments. Therefore, this paper proposes a dual-branch Diff-UNet architecture to solve the medical image segmentation problem. Specifically, this architecture introduces the Transformer internal network on top of the standard UNet architecture based on DPM and realizes the interaction of UNet and Transformer branch features through bidirectional connection units to capture local features and remote dependencies better. In addition, through the feature fusion module (FFM), the global context information extracted by DPM is combined with the local detail features captured by the segmentation network. Simultaneously, this paper introduces a mutual graph learning (MGL) network to decompose the image into two task-specific feature maps, which are used to roughly locate the object position and capture the fine details of the object boundary. Finally, the cross attention (CA) module combines the edge information of the diffusion model with the features of the segmentation network to enhance the network’s ability to perceive images. Experiments demonstrate the effectiveness of our Diff-UNet on challenging datasets, including self-collected databases and LUNA16.