Constrained Domain Adaptation for Segmentation

Bateson, Mathilde; Kervadec, Hoel; Dolz, José; Lombaert, Hervé; Ayed, Ismail Ben

doi:10.1007/978-3-030-32245-8_37

Cited by 30 publications

(16 citation statements)

References 20 publications

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“…They assume that the high-level features extracted by fullyconnected layers have large domain shift, thus CMD is imposed on these layers to perform adaptation. Bateson et al [116] propose an unsupervised constrained DA framework for disc MR image segmentation. They propose to use some useful prior knowledge that is invariant across domains as an inequality constraint, and impose such constraints on the predicted results of unlabeled target samples as the domain adaptation regularization.…”

Section: Unsupervised Deep Damentioning

confidence: 99%

Domain Adaptation for Medical Image Analysis: A Survey

Guan,

Liu

2021

Preprint

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Machine learning techniques used in computeraided medical image analysis usually suffer from the domain shift problem caused by different distributions between source/reference data and target data. As a promising solution, domain adaptation has attracted considerable attention in recent years. The aim of this paper is to survey the recent advances of domain adaptation methods in medical image analysis. We first present the motivation of introducing domain adaptation techniques to tackle domain heterogeneity issues for medical image analysis. Then we provide a review of recent domain adaptation models in various medical image analysis tasks. We categorize the existing methods into shallow and deep models, and each of them is further divided into supervised, semi-supervised and unsupervised methods. We also provide a brief summary of the benchmark medical image datasets that support current domain adaptation research. This survey will enable researchers to gain a better understanding of the current status, challenges and future directions of this energetic research field.

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Section: Unsupervised Deep Damentioning

confidence: 99%

Domain Adaptation for Medical Image Analysis: A Survey

Guan,

Liu

2021

Preprint

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“…Nevertheless, their formulation is limited to linear constraints. More recently, inequality constraints have been tackled by augmenting the learning objective with a penalty-based function, e.g., L 2 penalty, which can be imposed within a continuous optimization framework [5,18,19], or in the discrete domain [28]. Despite these methods have demonstrated remarkable performance in weakly supervised segmentation, they require that prior knowledge, exact or approximate, is given.…”

Section: Related Workmentioning

confidence: 99%

Looking at the whole picture: constrained unsupervised anomaly segmentation

Silva-Rodríguez¹,

Naranjo²,

Dolz³

2021

Preprint

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Current unsupervised anomaly localization approaches rely on generative models to learn the distribution of normal images, which is later used to identify potential anomalous regions derived from errors on the reconstructed images. However, a main limitation of nearly all prior literature is the need of employing anomalous images to set a class-specific threshold to locate the anomalies. This limits their usability in realistic scenarios, where only normal data is typically accessible. Despite this major drawback, only a handful of works have addressed this limitation, by integrating supervision on attention maps during training. In this work, we propose a novel formulation that does not require accessing images with abnormalities to define the threshold. Furthermore, and in contrast to very recent work, the proposed constraint is formulated in a more principled manner, leveraging well-known knowledge in constrained optimization. In particular, the equality constraint on the attention maps in prior work is replaced by an inequality constraint, which allows more flexibility. In addition, to address the limitations of penalty-based functions we employ an extension of the popular log-barrier methods to handle the constraint. Comprehensive experiments on the popular BRATS'19 dataset demonstrate that the proposed approach substantially outperforms relevant literature, establishing new state-of-the-art results for unsupervised lesion segmentation. IntroductionUnder the supervised learning paradigm, deep learning models have achieved astonishing performance in a wide range of applications. Nevertheless, a main limitation of these models is the large amount of labeled data required for training. Obtaining such curated labeled datasets is a cumbersome process prone to subjectivity, which makes access to sufficient training data difficult in practice. This problem is further magnified in the context of medical image segmentation, where labeling involves assigning a category to each image pixel

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“…Weakly-supervised learning methods can significantly reduce the cost of annotation that is needed to collect a training set. The methods differ by the type of weak annotation they rely on, such as image-level labels [12], points [2], partial labels [13] or global image statistics [1]. In this work, we build upon the recent papers that have focused on training neural networks using pseudo-annotations generated from bounding boxes.…”

Section: Related Workmentioning

confidence: 99%

Medical image segmentation with imperfect 3D bounding boxes

Redekop¹,

Chernyavskiy²

2021

Preprint

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The development of high quality medical image segmentation algorithms depends on the availability of large datasets with pixellevel labels. The challenges of collecting such datasets, especially in case of 3D volumes, motivate to develop approaches that can learn from other types of labels that are cheap to obtain, e.g. bounding boxes. We focus on 3D medical images with their corresponding 3D bounding boxes which are considered as series of per-slice non-tight 2D bounding boxes. While current weakly-supervised approaches that use 2D bounding boxes as weak labels can be applied to medical image segmentation, we show that their success is limited in cases when the assumption about the tightness of the bounding boxes breaks. We propose a new bounding box correction framework which is trained on a small set of pixel-level annotations to improve the tightness of a larger set of non-tight bounding box annotations. The effectiveness of our solution is demonstrated by evaluating a known weakly-supervised segmentation approach with and without the proposed bounding box correction algorithm. When the tightness is improved by our solution, the results of the weakly-supervised segmentation become much closer to those of the fully-supervised one.

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Constrained Domain Adaptation for Segmentation

Cited by 30 publications

References 20 publications

Domain Adaptation for Medical Image Analysis: A Survey

Domain Adaptation for Medical Image Analysis: A Survey

Looking at the whole picture: constrained unsupervised anomaly segmentation

Medical image segmentation with imperfect 3D bounding boxes

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