English

Lesion Analysis and Diagnosis with Mask-RCNN

Image and Video Processing 2018-07-18 v2 Machine Learning

Abstract

This project applies Mask R-CNN method to ISIC 2018 challenge tasks: lesion boundary segmentation (task1), lesion attributes detection (task 2), lesion diagnosis (task 3), a solution to the latter is using a trained model for task 1 and a simple voting procedure.

Cite

@article{arxiv.1807.05979,
  title  = {Lesion Analysis and Diagnosis with Mask-RCNN},
  author = {Andrey Sorokin},
  journal= {arXiv preprint arXiv:1807.05979},
  year   = {2018}
}

Comments

4 pages, 4 figures, ISIC 2018 challenge

R2 v1 2026-06-23T03:03:01.018Z