First, it learns how to generate synthetic images from segmentation masks, which are essentially color-coded overlays that tell an algorithm which parts of an image are, say, healthy or diseased.

This is the first experiment of Image Segmentation for Ovarian-Tumor-2D Multiclass based on our TensorFlowFlexUNet (TensorFlow Flexible UNet Image Segmentation Model for Multiclass) and an ...

A new technical paper titled “Scanning electron microscopy-based automatic defect inspection for semiconductor manufacturing: a systematic review” was published by researchers at KU Leuven and imec.

Aiming at the situation of dermatoscopic images with fuzzy lesion boundaries, variable morphology and high similarity to background, this paper proposes a skin lesion segmentation algorithm that ...

In this context, by building datasets using deep learning models, we propose a novel stripe segmentation algorithm for oceanic internal waves, leveraging synthetic aperture radar (SAR) images based on ...

In structural MRI, the signal-to-noise ratio is critical. Discover how denoising algorithms can help to obtain clearer results.

Artificial intelligence has the potential to improve the analysis of medical image data. For example, algorithms based on deep learning can determine the location and size of tumors.

Automated object segmentation represents a specific goal for image segmentation algorithms, in which pixels within the same object are grouped together. Finding objects in a data-driven manner allows ...