Difficulty-Aware Glaucoma Classification with Multi-rater Consensus Modeling

Yu, Shuang; Ma, Kai; Bian, Cheng; Chu, Chunyan; Liu, Hanruo; Zheng, Yefeng

doi:10.1007/978-3-030-59710-8_72

Cited by 18 publications

(10 citation statements)

References 14 publications

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“…• predicts the sensitive, specific, balanced result merged for images of glaucoma in [193] Additional clinical diagnosis reports (abstract descriptions)…”

Section: Other Types Of Informationmentioning

confidence: 99%

Deep Learning Application for Analyzing of Constituents and Their Correlations in the Interpretations of Medical Images

et al. 2021

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The need for time and attention, given by the doctor to the patient, due to the increased volume of medical data to be interpreted and filtered for diagnostic and therapeutic purposes has encouraged the development of the option to support, constructively and effectively, deep learning models. Deep learning (DL) has experienced an exponential development in recent years, with a major impact on interpretations of the medical image. This has influenced the development, diversification and increase of the quality of scientific data, the development of knowledge construction methods and the improvement of DL models used in medical applications. All research papers focus on description, highlighting, classification of one of the constituent elements of deep learning models (DL), used in the interpretation of medical images and do not provide a unified picture of the importance and impact of each constituent in the performance of DL models. The novelty in our paper consists primarily in the unitary approach, of the constituent elements of DL models, namely, data, tools used by DL architectures or specifically constructed DL architecture combinations and highlighting their “key” features, for completion of tasks in current applications in the interpretation of medical images. The use of “key” characteristics specific to each constituent of DL models and the correct determination of their correlations, may be the subject of future research, with the aim of increasing the performance of DL models in the interpretation of medical images.

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“…• predicts the sensitive, specific, balanced result merged for images of glaucoma in [193] Additional clinical diagnosis reports (abstract descriptions)…”

Section: Other Types Of Informationmentioning

confidence: 99%

Deep Learning Application for Analyzing of Constituents and Their Correlations in the Interpretations of Medical Images

et al. 2021

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“… Furthermore, we provide an intuitive interpretation that is more consistent with the multirater labels segmentation task: uncertainty reflects pixelwise image difficulty, where areas with high difficulty are more challenging for the model to accurately segment. Image difficulty, which is related to the visual characteristics of the images, such as image quality and occlusion of the area of the lesions, is one of the causal factors of inter-rater variability [ 15 ]. As demonstrated in Figure 1(a) , the blood vessels occluding the edge region of the OD not only make ophthalmologists' judgment difficult but also hinder the accurate prediction of the deep neural network.…”

Section: Introductionmentioning

confidence: 99%

A Soft Label Method for Medical Image Segmentation with Multirater Annotations

Zhang

Zheng

Shi

2023

Computational Intelligence and Neuroscience

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In medical image analysis, collecting multiple annotations from different clinical raters is a typical practice to mitigate possible diagnostic errors. For such multirater labels’ learning problems, in addition to majority voting, it is a common practice to use soft labels in the form of full-probability distributions obtained by averaging raters as ground truth to train the model, which benefits from uncertainty contained in soft labels. However, the potential information contained in soft labels is rarely studied, which may be the key to improving the performance of medical image segmentation with multirater annotations. In this work, we aim to improve soft label methods by leveraging interpretable information from multiraters. Considering that mis-segmentation occurs in areas with weak supervision of annotations and high difficulty of images, we propose to reduce the reliance on local uncertain soft labels and increase the focus on image features. Therefore, we introduce local self-ensembling learning with consistency regularization, forcing the model to concentrate more on features rather than annotations, especially in regions with high uncertainty measured by the pixelwise interclass variance. Furthermore, we utilize a label smoothing technique to flatten each rater’s annotation, alleviating overconfidence of structural edges in annotations. Without introducing additional parameters, our method improves the accuracy of the soft label baseline by 4.2% and 2.7% on a synthetic dataset and a fundus dataset, respectively. In addition, quantitative comparisons show that our method consistently outperforms existing multirater strategies as well as state-of-the-art methods. This work provides a simple yet effective solution for the widespread multirater label segmentation problems in clinical diagnosis.

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“…Clinically, it is often necessary to synthesize the opinions of several different clinical experts as a reference. In the practice of medical image processing, prior work [3,29,56] called it 'multi-rater problem', which means that each instance of the collected dataset is annotated by several different raters. One multi-rater example of optic cup segmentation is shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Opinions Vary? Diagnosis First!

Wu¹,

Fang²,

Yang³

et al. 2022

Preprint

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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.

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Difficulty-Aware Glaucoma Classification with Multi-rater Consensus Modeling

Cited by 18 publications

References 14 publications

Deep Learning Application for Analyzing of Constituents and Their Correlations in the Interpretations of Medical Images

Deep Learning Application for Analyzing of Constituents and Their Correlations in the Interpretations of Medical Images

A Soft Label Method for Medical Image Segmentation with Multirater Annotations

Opinions Vary? Diagnosis First!

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