A Semantic Segmentation Algorithm for Pleural Effusion Based on DBIF-AUNet

Pleural effusion semantic segmentation can significantly enhance the accuracy and timeliness of clinical diagnosis and treatment by precisely identifying disease severity and lesion areas. Currently, semantic segmentation of pleural effusion CT images faces multiple challenges. These include similar gray levels between effusion and surrounding tissues, blurred edges, and variable morphology. Existing methods often struggle with diverse image variations and complex edges, primarily because direct feature concatenation causes semantic gaps. To address these challenges, we propose the Dual-Branch Interactive Fusion Attention model (DBIF-AUNet). This model constructs a densely nested skip-connection network and innovatively refines the Dual-Domain Feature Disentanglement module (DDFD). The DDFD module orthogonally decouples the functions of dual-domain modules to achieve multi-scale feature complementarity and enhance characteristics at different levels. Concurrently, we design a Branch Interaction Attention Fusion module (BIAF) that works synergistically with the DDFD. This module dynamically weights and fuses global, local, and frequency band features, thereby improving segmentation robustness. Furthermore, we implement a nested deep supervision mechanism with hierarchical adaptive hybrid loss to effectively address class imbalance. Through validation on 1,622 pleural effusion CT images from Southwest Hospital, DBIF-AUNet achieved IoU and Dice scores of 80.1% and 89.0% respectively. These results outperform state-of-the-art medical image segmentation models U-Net++ and Swin-UNet by 5.7%/2.7% and 2.2%/1.5% respectively, demonstrating significant optimization in segmentation accuracy for complex pleural effusion CT images.

A Method for the Architecture of a Medical Vertical Large Language Model Based on Deepseek R1

In recent years, despite foundation models like DeepSeek-R1 and ChatGPT demonstrating significant capabilities in general tasks, professional knowledge barriers, computational resource requirements, and deployment environment limitations have severely hindered their application in actual medical scenarios. Addressing these challenges, this paper proposes an efficient lightweight medical vertical large language model architecture method, systematically solving the lightweight problem of medical large models from three dimensions: knowledge acquisition, model compression, and computational optimization. At the knowledge acquisition level, a knowledge transfer pipeline is designed from the fine-tuned DeepSeek-R1-Distill-70B teacher model to the DeepSeek-R1-Distill-7B student model, and Low-Rank Adaptation (LoRA) technology is adopted to precisely adjust key attention layers. At the model compression level, compression techniques including 4-bit weight quantization are implemented while preserving the core representation ability for medical reasoning. At the computational optimization level, inference optimization techniques such as Flash Attention acceleration and continuous batching are integrated, and a professional prompt template system is constructed to adapt to different types of medical problems. Experimental results on medical question-answering datasets show that the method proposed in this paper maintains professional accuracy while reducing memory consumption by 64.7\% and inference latency by 12.4\%, providing an effective solution for the application of medical large models in resource-constrained environments such as edge computing devices.