Opinions Vary? Diagnosis First!

02/14/2022
by   Junde Wu, et al.
0

In medical image segmentation, images are usually annotated by several different clinical experts. This clinical routine helps to mitigate the personal bias. However, Computer Vision models often assume there has a unique ground-truth for each of the instance. This research gap between Computer Vision and medical routine is commonly existed but less explored by the current research.In this paper, we try to answer the following two questions: 1. How to learn an optimal combination of the multiple segmentation labels? and 2. How to estimate this segmentation mask from the raw image? We note that in clinical practice, the image segmentation mask usually exists as an auxiliary information for disease diagnosis. Adhering to this mindset, we propose a framework taking the diagnosis result as the gold standard, to estimate the segmentation mask upon the multi-rater segmentation labels, named DiFF (Diagnosis First segmentation Framework).DiFF is implemented by two novelty techniques. First, DFSim (Diagnosis First Simulation of gold label) is learned as an optimal combination of multi-rater segmentation labels for the disease diagnosis. Then, toward estimating DFSim mask from the raw image, we further propose T&G Module (Take and Give Module) to instill the diagnosis knowledge into the segmentation network. The experiments show that compared with commonly used majority vote, the proposed DiFF is able to segment the masks with 6 improvement on diagnosis AUC score, which also outperforms various state-of-the-art multi-rater methods by a large margin.

READ FULL TEXT
research
08/05/2022

Calibrate the inter-observer segmentation uncertainty via diagnosis-first principle

On the medical images, many of the tissues/lesions may be ambiguous. Tha...
research
12/01/2022

Multi-rater Prism: Learning self-calibrated medical image segmentation from multiple raters

In medical image segmentation, it is often necessary to collect opinions...
research
06/10/2022

Learning self-calibrated optic disc and cup segmentation from multi-rater annotations

The segmentation of optic disc(OD) and optic cup(OC) from fundus images ...
research
11/16/2019

Liver Steatosis Segmentation with Deep Learning Methods

Liver steatosis is known as the abnormal accumulation of lipids within c...
research
02/19/2023

SEMI-PointRend: Improved Semiconductor Wafer Defect Classification and Segmentation as Rendering

In this study, we applied the PointRend (Point-based Rendering) method t...
research
09/28/2017

Fast Barcode Retrieval for Consensus Contouring

Marking tumors and organs is a challenging task suffering from both inte...
research
08/29/2023

Shape-Margin Knowledge Augmented Network for Thyroid Nodule Segmentation and Diagnosis

Thyroid nodule segmentation is a crucial step in the diagnostic procedur...

Please sign up or login with your details

Forgot password? Click here to reset