Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis

Cheplygina, Veronika; Bruijne, Marleen de; Pluim, Josien P. W.

doi:10.1016/j.media.2019.03.009

Cited by 743 publications

(474 citation statements)

References 150 publications

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“…Yi et al (2018) broadly investigated the use of GANs in medical imaging. Cheplygina et al (2019) reviewed semi-supervised, multi-instance learning, and transfer learning in medical image analysis, covering both deep learning and traditional segmentation methods. Hesamian et al (2019) surveyed deep learning techniques suggested for medical image segmentation but with a particular focus on architectural advancements and training schemes.…”

Section: Related Workmentioning

confidence: 99%

Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation

Tajbakhsh¹,

Jeyaseelan²,

Li³

et al. 2020

Medical Image Analysis

722

358

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The medical imaging literature has witnessed remarkable progress in high-performing segmentation models based on convolutional neural networks. Despite the new performance highs, the recent advanced segmentation models still require large, representative, and high quality annotated datasets. However, rarely do we have a perfect training dataset, particularly in the field of medical imaging, where data and annotations are both expensive to acquire. Recently, a large body of research has studied the problem of medical image segmentation with imperfect datasets, tackling two major dataset limitations: scarce annotations where only limited annotated data is available for training, and weak annotations where the training data has only sparse annotations, noisy annotations, or image-level annotations. In this article, we provide a detailed review of the solutions above, summarizing both the technical novelties and empirical results. We further compare the benefits and requirements of the surveyed methodologies and provide our recommended solutions to the problems of scarce and weak annotations. We hope this review increases the community awareness of the techniques to handle imperfect datasets.

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Section: Related Workmentioning

confidence: 99%

Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation

Tajbakhsh¹,

Jeyaseelan²,

Li³

et al. 2020

Medical Image Analysis

722

358

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“…A powerful property of disentangled representations is that they can be applied in semi-supervised learning [Almahairi et al, 2018]. An important application in medical image analysis is (semi-supervised) segmentation, for a recent review see Cheplygina et al [2018]. As discussed in this review, manual segmentations are a laborious task, particularly as inter-rater variation means multiple labels are required to reach a consensus, and images labelled by multiple experts are very limited.…”

Section: Semi-supervised Segmentationmentioning

confidence: 99%

Disentangled representation learning in cardiac image analysis

Chartsias

Joyce

Papanastasiou³

et al. 2019

Medical Image Analysis

162

143

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Typically, a medical image offers spatial information on the anatomy (and pathology) modulated by imaging specific characteristics. Many imaging modalities including Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) can be interpreted in this way. We can venture further and consider that a medical image naturally factors into some spatial factors depicting anatomy and factors that denote the imaging characteristics. Here, we explicitly learn this decomposed (disentangled) representation of imaging data, focusing in particular on cardiac images. We propose Spatial Decomposition Network (SDNet), which factorises 2D medical images into spatial anatomical factors and non-spatial modality factors. We demonstrate that this high-level representation is ideally suited for several medical image analysis tasks, such as semi-supervised segmentation, multi-task segmentation and regression, and image-to-image synthesis. Specifically, we show that our model can match the performance of fully supervised segmentation models, using only a fraction of the labelled images. Critically, we show that our factorised representation also benefits from supervision obtained either when we use auxiliary tasks to train the model in a multi-task setting (e.g. regressing to known cardiac indices), or when aggregating multimodal data from different sources (e.g. pooling together MRI and CT data). To explore the properties of the learned factorisation, we perform latent-space arithmetic and show that we can synthesise CT from MR and vice versa, by swapping the modality factors. We also demonstrate that the factor holding image specific information can be used to predict the input modality with high accuracy. Code will be made available at https://github. com/agis85/anatomy_modality_decomposition.

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“…First, there are two articles covering visual domain adaptation [24], [25], with a third one specializing in deep learning [26]. Secondly, there is an empirical comparison of domain adaptation methods for genomic sequence analysis [27] and thirdly, a survey paper on, amongst others, transfer learning in biomedical imaging [28].…”

Section: Scopementioning

confidence: 99%

A Review of Domain Adaptation without Target Labels

Kouw

Loog

2021

IEEE Trans. Pattern Anal. Mach. Intell.

415

213

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Domain adaptation has become a prominent problem setting in machine learning and related fields. This review asks the question: how can a classifier learn from a source domain and generalize to a target domain? We present a categorization of approaches, divided into, what we refer to as, sample-based, feature-based and inference-based methods. Sample-based methods focus on weighting individual observations during training based on their importance to the target domain. Feature-based methods revolve around on mapping, projecting and representing features such that a source classifier performs well on the target domain and inference-based methods incorporate adaptation into the parameter estimation procedure, for instance through constraints on the optimization procedure. Additionally, we review a number of conditions that allow for formulating bounds on the cross-domain generalization error. Our categorization highlights recurring ideas and raises questions important to further research.

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Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis

Cited by 743 publications

References 150 publications

Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation

Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation

Disentangled representation learning in cardiac image analysis

A Review of Domain Adaptation without Target Labels

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