Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet

06/29/2020
by   Chuang Zhu, et al.
0

The automatic and objective medical diagnostic model can be valuable to achieve early cancer detection, and thus reducing the mortality rate. In this paper, we propose a highly efficient multi-level malignant tissue detection through the designed adversarial CAC-UNet. A patch-level model with a pre-prediction strategy and a malignancy area guided label smoothing is adopted to remove the negative WSIs, with which to lower the risk of false positive detection. For the selected key patches by multi-model ensemble, an adversarial context-aware and appearance consistency UNet (CAC-UNet) is designed to achieve robust segmentation. In CAC-UNet, mirror designed discriminators are able to seamlessly fuse the whole feature maps of the skillfully designed powerful backbone network without any information loss. Besides, a mask prior is further added to guide the accurate segmentation mask prediction through an extra mask-domain discriminator. The proposed scheme achieves the best results in MICCAI DigestPath2019 challenge on colonoscopy tissue segmentation and classification task. The full implementation details and the trained models are available at https://github.com/Raykoooo/CAC-UNet.

READ FULL TEXT

page 2

page 4

page 5

page 11

page 13

page 14

research
04/06/2023

Multi-task learning for tissue segmentation and tumor detection in colorectal cancer histology slides

Automating tissue segmentation and tumor detection in histopathology ima...
research
06/29/2023

ReMaX: Relaxing for Better Training on Efficient Panoptic Segmentation

This paper presents a new mechanism to facilitate the training of mask t...
research
02/14/2023

YOWOv2: A Stronger yet Efficient Multi-level Detection Framework for Real-time Spatio-temporal Action Detection

Designing a real-time framework for the spatio-temporal action detection...
research
07/25/2019

NoduleNet: Decoupled False Positive Reductionfor Pulmonary Nodule Detection and Segmentation

Pulmonary nodule detection, false positive reduction and segmentation re...
research
02/20/2020

Cross-stained Segmentation from Renal Biopsy Images Using Multi-level Adversarial Learning

Segmentation from renal pathological images is a key step in automatic a...
research
12/23/2021

Omni-Seg: A Single Dynamic Network for Multi-label Renal Pathology Image Segmentation using Partially Labeled Data

Computer-assisted quantitative analysis on Giga-pixel pathology images h...
research
04/26/2022

Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images

Multiple Instance Learning (MIL) methods have become increasingly popula...

Please sign up or login with your details

Forgot password? Click here to reset