Medical image segmentation plays an important role in clinical decision making, treatment planning, and disease tracking. However, it still faces two major challenges. On the one hand, there is often a "soft boundary" between foreground and background in medical images, with poor illumination and low contrast further reducing the distinguishability of foreground and background within the image. On the other hand, co-occurrence phenomena are widespread in medical images, and learning these features is misleading to the model's judgment. To address these challenges, we propose a general framework called Contrast-Driven Medical Image Segmentation (ConDSeg). First, we develop a contrastive training strategy called Consistency Reinforcement. It is designed to improve the encoder's robustness in various illumination and contrast scenarios, enabling the model to extract high-quality features even in adverse environments. Second, we introduce a Semantic Information Decoupling module, which is able to decouple features from the encoder into foreground, background, and uncertainty regions, gradually acquiring the ability to reduce uncertainty during training. The Contrast-Driven Feature Aggregation module then contrasts the foreground and background features to guide multi-level feature fusion and key feature enhancement, further distinguishing the entities to be segmented. We also propose a Size-Aware Decoder to solve the scale singularity of the decoder. It accurately locate entities of different sizes in the image, thus avoiding erroneous learning of co-occurrence features. Extensive experiments on five datasets across three scenarios demonstrate the state-of-the-art performance of our method, proving its advanced nature and general applicability to various medical image segmentation scenarios.

In recent years, crowd counting has garnered increasing attention due to its wide range of societal applications. However, the vast differences in crowd density distributions across various areas within a scene make it difficult for existing crowd counting methods to achieve satisfactory performance. To address this issue, this letter introduces a novel method called Dynamic Density-Region-Aware Network (DDRANet). First, a Density-Aware Module (DAM) is designed to detect density variations and dynamically segment the image into multiple density regions. Subsequently, the Region Information Encoder (RIE) transforms the obtained density region map into a latent space to enhance the information of density regions. Then, through a specially designed Region Attention Block (RAB), the encoded density region map guides the model to focus on different density regions separately, enabling the model to adapt to the density variations of the crowd within the image. Furthermore, we combine Gaussian kernel function and Density Peak Clustering to automatically generate ground truth of density region maps, supervising the optimization of DAM. Extensive experimental results on three datasets demonstrate DDRANet's superior performance over other state-of-the-art methods.