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In 1897, French police faced a difficult problem: a serial killer named Joseph Vacher was stealing and murdering sheperds, and remained impossible to catch....

In the diverse field of medical imaging, automatic segmentation has numerous applications and must handle a wide variety of input domains, such as different...

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Prof.dr. K.J. Batenburg dr. D.M. Pelt dr. O. Dzyubachyk

This high-level information is useful in computer vision applications, like the self-driving robot described in the QDN learning resource.

The similar shape and texture of colonic polyps and normal mucosal tissues lead to low accuracy of medical image segmentation algorithms. To solve these prob...

The experiments allowed by current machine learning models imply a revival of the debate on the causes of specific trends of human visual psychophysics. Mach...

Jun 2, 2025 - Explore image segmentation's impact on computer vision. Learn techniques ranging from traditional methods to deep learning innovations.

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Dilated Convolution for Semantic Image Segmentation - fyu/dilation

Biomedical image processing has seen a significant improvement due to the availability of advanced technologies to capture high resolution images. This leads...

Apr 3, 2024 - Combining the best of both of specialist (e.g., SimpleClick) and generalist (e.g., SAM) designs to achieve all of low latency, high quality, and diverse...

Medical image segmentation is a fundamental task for medical image analysis and surgical planning. In recent years, UNet-based networks have prevailed in the...

Currently, most mask extraction techniques are based on convolutional neural networks (CNNs). However, there are still numerous problems that mask extraction...

Integrating multi-modal data to promote medical image analysis has recently gained great attention. This paper presents a novel scheme to learn the mutual...

Referring image segmentation aims to segment a referent via a natural linguistic expression. Due to the distinct data properties between text and image, it is...