IEEE

Universal Image Segmentation With Efficiency

Abstract: In this paper, we present UISE, a unified image segmentation framework that achieves efficient performance across various segmentation tasks, eliminating the need for multiple specialized ...
IEEE

Text-Driven Medical Image Segmentation With Text-Free Inference via Knowledge Distillation

Abstract: Medical image segmentation plays a pivotal role in ensuring accurate diagnosis. Traditional methods are predominantly monomodal, relying solely on image data. These image-only methods ...
GitHub

SegDINO: An Efficient Design for Medical and Natural Image Segmentation with DINO-V3

We propose SegDINO, an efficient image segmentation framework that couples a frozen DINOv3 backbone with a lightweight MLP decoder, achieving state-of-the-art performance on both medical and natural ...
Medical Xpress

Semi-supervised segmentation method developed for 3D medical image accuracy

A research team led by Prof. Wang Huanqin at the Institute of Intelligent Machines, the Hefei Institutes of Physical Science of the Chinese Academy of Sciences, recently proposed a semi-supervised ...
GitHub

Solution : Error code: 400 - {'object': 'error', 'message': 'Unknown image model type: dots_ocr', 'type': 'BadRequestError', 'param': None, 'code': 400}

when i've ran the model using vllm i have received this error message : openai.BadRequestError: Error code: 400 - {'object': 'error', 'message': 'Unknown image model ...