mlsanityData to 14 Oct 2026

Enhancing Diagnosis through AI-driven Analysis of Reflectance Confocal Microscopy

arXiv 2404.16080 ↗First posted 24 Apr 2024eess.IV cs.AI cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Reflectance Confocal Microscopy (RCM) is a non-invasive imaging technique used in biomedical research and clinical dermatology. It provides virtual high-resolution images of the skin and superficial tissues, reducing the need for physical biopsies. RCM employs a laser light source to illuminate the tissue, capturing the reflected light to generate detailed images of microscopic structures at various depths. Recent studies explored AI and machine learning, particularly CNNs, for analyzing RCM images. Our study proposes a segmentation strategy based on textural features to identify clinically significant regions, empowering dermatologists in effective image interpretation and boosting diagnostic confidence. This approach promises to advance dermatological diagnosis and treatment.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 18 references that are in the map. First posted on arXiv 24 Apr 2024.

Builds on

6 of 18 references.

References · 6

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  4. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,968
  5. 2016Densely Connected Convolutional Networkscited by 6,720
  6. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,750

Built on by

Not cited yet.