UD-MIL: Uncertainty-Driven Deep Multiple Instance Learning for OCT Image Classification

Wang, Xi; Tang, Florence; Chen, Hao; Luo, Luyang; Tang, Zhangjun; Ran, An-Ran; Cheung, Carol Y.; Heng, Pheng‐Ann

doi:10.1109/jbhi.2020.2983730

Cited by 65 publications

(30 citation statements)

References 42 publications

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“…Firstly, we designed a diagnostic model, named GastroMIL, to distinguish GC images from normal gastric tissue images. In order to avoid complex manual annotation, we applied weak supervised learning to our algorithm framework, specifically multiple instance learning (MIL) [26] , [27] , [28] , [29] . Based on the assumption of MIL, each input image was a bag, and the tiles it contained were the example instances.…”

Section: Methodsmentioning

confidence: 99%

Accurate diagnosis and prognosis prediction of gastric cancer using deep learning on digital pathological images: A retrospective multicentre study

et al. 2021

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Background: To reduce the high incidence and mortality of gastric cancer (GC), we aimed to develop deep learning-based models to assist in predicting the diagnosis and overall survival (OS) of GC patients using pathological images. Methods: 2333 hematoxylin and eosin-stained pathological pictures of 1037 GC patients were collected from two cohorts to develop our algorithms, Renmin Hospital of Wuhan University (RHWU) and the Cancer Genome Atlas (TCGA). Additionally, we gained 175 digital pictures of 91 GC patients from National Human Genetic Resources Sharing Service Platform (NHGRP), served as the independent external validation set. Two models were developed using artificial intelligence (AI), one named GastroMIL for diagnosing GC, and the other named MIL-GC for predicting outcome of GC. Findings: The discriminatory power of GastroMIL achieved accuracy 0.920 in the external validation set, superior to that of the junior pathologist and comparable to that of expert pathologists. In the prognostic model, Cindices for survival prediction of internal and external validation sets were 0.671 and 0.657, respectively. Moreover, the risk score output by MIL-GC in the external validation set was proved to be a strong predictor of OS both in the univariate (HR = 2.414, P < 0.0001) and multivariable (HR = 1.803, P = 0.043) analyses. The predicting process is available at an online website (https://baigao.github.io/Pathologic-Prognostic-Analysis/). Interpretation: Our study developed AI models and contributed to predicting precise diagnosis and prognosis of GC patients, which will offer assistance to choose appropriate treatment to improve the survival status of GC patients. Funding: Not applicable.

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Section: Methodsmentioning

confidence: 99%

Accurate diagnosis and prognosis prediction of gastric cancer using deep learning on digital pathological images: A retrospective multicentre study

et al. 2021

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“…on 3D OCT volumes using three inputs, original 3D-OCT cube and the other two are computed during training guided by 3D grad-CAM heatmaps. Wang et al [12] proposes a weakly deep supervised learning framework with uncertainty estimation to address the macula-related disease classification problem from OCT images with the only volume level label. It eliminates the need to obtain fine-grained expert annotations, which is usually quite difficult and expensive.…”

Section: Guest Editorial Ophthalmic Image Analysis and Informaticsmentioning

confidence: 99%

Guest Editorial Ophthalmic Image Analysis and Informatics

Cheng¹,

Fu²,

DeBuc³

et al. 2020

IEEE J. Biomed. Health Inform.

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“…Despite the achievement, these approaches do not ensure robust learning from samples with low uncertainty. To reduce the influence of uncertain samples, uncertainty guidance has been introduced into the literature of SSL [15,30,33,34]. Nevertheless, semi-supervised segmentation of COVID-19 lesions remains a challenging task, of which the annotations are extremely scarce, and the lesions often have irregular and ambiguous contours.…”

Section: Introductionmentioning

confidence: 99%

Dual-Consistency Semi-supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images

Li¹,

Luo

Lin

et al. 2021

Lecture Notes in Computer Science

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The novel coronavirus disease 2019 (COVID-19) characterized by atypical pneumonia has caused millions of deaths worldwide. Automatically segmenting lesions from chest Computed Tomography (CT) is a promising way to assist doctors in COVID-19 screening, treatment planning, and follow-up monitoring. However, voxel-wise annotations are extremely expert-demanding and scarce, especially when it comes to novel diseases, while an abundance of unlabeled data could be available. To tackle the challenge of limited annotations, in this paper, we propose an uncertainty-guided dual-consistency learning network (UDC-Net) for semi-supervised COVID-19 lesion segmentation from CT images. Specifically, we present a dual-consistency learning scheme that simultaneously imposes image transformation equivalence and feature perturbation invariance to effectively harness the knowledge from unlabeled data. We then quantify the segmentation uncertainty in two forms and employ them together to guide the consistency regularization for more reliable unsupervised learning. Extensive experiments showed that our proposed UDC-Net improves the fully supervised method by 6.3% in Dice and outperforms other competitive semi-supervised approaches by Y. Li and L. Luo-The first two authors contributed equally.

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UD-MIL: Uncertainty-Driven Deep Multiple Instance Learning for OCT Image Classification

Cited by 65 publications

References 42 publications

Accurate diagnosis and prognosis prediction of gastric cancer using deep learning on digital pathological images: A retrospective multicentre study

Accurate diagnosis and prognosis prediction of gastric cancer using deep learning on digital pathological images: A retrospective multicentre study

Guest Editorial Ophthalmic Image Analysis and Informatics

Dual-Consistency Semi-supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images

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