Search

everythingexperts & clinicslearnfaqsresearchcommunity

for

Search:↓

This study introduced a novel, weakly supervised method for segmenting hair in Scanning Electron Microscope images using simple image-level annotations, achieving over 30% improvement in mean Hausdorff Distance compared to Unet and SAM, while enhancing interpretability and refinement.

This study proposes a new method for hair segmentation that improved performance in skin lesion images, as indicated by an increase in the Dice score from 76.97% to 79.08%.

This study reports that improvements to the EczemaNet pipeline, incorporating pixel-level segmentation and data augmentation, enhanced the reliability and interpretability of assessing atopic dermatitis severity from digital images.

In this study, the proposed MSF-VMDNet, combining dual encoder networks with a multi-frequency domain mechanism, significantly outperformed existing methods in segmenting skin cancer tissues from histological slide images, achieving high accuracy with an MIoU of 95.37% and a Dice coefficient of 95.11%.

This study describes a new method using ImageJ to quantitatively assess hair loss in mice with alopecia areata, offering a more precise and reproducible alternative to traditional visual scoring systems by employing image-based analysis techniques.