A new artificial intelligence (AI) tool could make it much easier-and cheaper-for doctors and researchers to train medical imaging software, even when only a small number of patient scans are ...

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 ...

Abstract: 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 ...

The study of oceanic internal waves remains a critical area of research within oceanography. With the rapid advancements in oceanic remote sensing and deep learning, it is now possible to extract ...

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 ...