2021
DOI: 10.1038/s41467-021-26216-9
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Annotation-efficient deep learning for automatic medical image segmentation

Abstract: Automatic medical image segmentation plays a critical role in scientific research and medical care. Existing high-performance deep learning methods typically rely on large training datasets with high-quality manual annotations, which are difficult to obtain in many clinical applications. Here, we introduce Annotation-effIcient Deep lEarning (AIDE), an open-source framework to handle imperfect training datasets. Methodological analyses and empirical evaluations are conducted, and we demonstrate that AIDE surpas… Show more

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Cited by 115 publications
(50 citation statements)
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“…There are fewer studies using deep learning methods to segment breast tumors using DCE-MRI than using mammograms, partly due to the availability of very large mammography datasets [12]. Studies based on DCE-MRI used wellestablished CNN segmentation models [13][14][15][16] based on U-Net [5], DeepMedic [17], or SegNet [18] architectures or less common models [19,20]. Several studies [14,16,19,21] took advantage of all the information given by the DCE-MRI by using the different post-contrast or subtraction (post-contrast minus the pre-contrast acquisition) images.…”
Section: Introductionmentioning
confidence: 99%
“…There are fewer studies using deep learning methods to segment breast tumors using DCE-MRI than using mammograms, partly due to the availability of very large mammography datasets [12]. Studies based on DCE-MRI used wellestablished CNN segmentation models [13][14][15][16] based on U-Net [5], DeepMedic [17], or SegNet [18] architectures or less common models [19,20]. Several studies [14,16,19,21] took advantage of all the information given by the DCE-MRI by using the different post-contrast or subtraction (post-contrast minus the pre-contrast acquisition) images.…”
Section: Introductionmentioning
confidence: 99%
“…Second, semi-automatic tumor segmentation contained complex operation and possibly man-made interference. A more stable and time-saving method such as automatic segmentation could be applied to the radiomics analysis [ 50 ]. Third, our study did not involve nuclear imaging, which limits our further clinical outreach and application.…”
Section: Discussionmentioning
confidence: 99%
“…Reproduced with permission. [ 112 ] Copyright 2021, Springer Nature. Object detection reproduced under terms of the CC‐BY license.…”
Section: Ai‐based Systemmentioning
confidence: 99%
“…As a way of emulating the human visual system, computer vision aims to develop an automated machine that can realize the tasks required for visual cognition. [112][113][114][115] In this article, we take a few examples on the recent progress of vision sensors, as shown in Figure 2. The first popular application of computer vision is object detection, which is a computer technology related to visual computing and image processing.…”
Section: Vision Sensor Applicationmentioning
confidence: 99%