Abstract: Deep learning models for medical image segmentation often struggle with task-specific characteristics, limiting their generalization to unseen tasks with new anatomies, labels, or modalities ...

Abstract: This research addresses the unique challenges of underwater image segmentation, such as reduced visibility, color distortion, and complex backgrounds. A key novelty of this work lies in the ...

We enforce this behavior by introducing a series of constraints into the proposed model architecture: (i) information from the support set, which includes the segmentation masks, is encoded by an ...

Meta Platforms Inc. today is expanding its suite of open-source Segment Anything computer vision models with the release of SAM 3 and SAM 3D, introducing enhanced object recognition and ...

A Department of Homeland Security spokeswoman offered a two-word reply Friday in response to a local news report that said immigration agents were seen wearing Halloween masks in the Los Angeles area.

To enhance model performance and generalization for the segmentation task, we first divided the original 405 images into training and testing subsets at an 8:2 ratio. Subsequently, a variety of data ...

Brain tumor detection and segmentation are critical tasks in medical imaging analysis for diagnosis and treatment planning. In recent years, computer vision techniques, particularly those implemented ...

The MHSAttResDU-Net incorporates RCC for complexity control and improved generalization under varying lighting. The SSRP unit in encoder-decoder blocks reduces feature map dimensions, capturing key ...

Abstract: Medical image segmentation has made signiffcant strides with the development of basic models. Speciffcally, models that combine CNNs with transformers can successfully extract both local and ...