Lesion Analysis and Diagnosis with Mask-RCNN

07/10/2018
by   Andrey Sorokin, et al.
0

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.

READ FULL TEXT

page 2

page 3

page 4

research
07/22/2018

Skin Lesion Analysis Towards Melanoma Detection via End-to-end Deep Learning of Convolutional Neural Networks

This article presents the design, experiments and results of our solutio...
research
10/26/2021

W-Net: A Two-Stage Convolutional Network for Nucleus Detection in Histopathology Image

Pathological diagnosis is the gold standard for cancer diagnosis, but it...
research
08/19/2019

Deep Active Lesion Segmentation

Lesion segmentation is an important problem in computer-assisted diagnos...
research
05/13/2021

Stroke Lesion Segmentation with Visual Cortex Anatomy Alike Neural Nets

Cerebrovascular accident or stroke, is an acute disease with extreme imp...
research
07/17/2020

Leveraging both Lesion Features and Procedural Bias in Neuroimaging: An Dual-Task Split dynamics of inverse scale space

The prediction and selection of lesion features are two important tasks ...
research
01/26/2019

Soft labeling by Distilling Anatomical knowledge for Improved MS Lesion Segmentation

This paper explores the use of a soft ground-truth mask ("soft mask") to...
research
10/20/2019

SANet:Superpixel Attention Network for Skin Lesion Attributes Detection

The accurate detection of lesion attributes is meaningful for both the c...

Please sign up or login with your details

Forgot password? Click here to reset