Self-Supervised Learning Based on Spatial Awareness for Medical Image Analysis

Nguyen, Xuan-Bac; Lee, Guee Sang; Kim, Sung Hyun; Yang, Hyung Jeong

doi:10.1109/access.2020.3021469

Cited by 37 publications

(25 citation statements)

References 32 publications

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“…Second, the acquired knowledge is transferred and the model is fine tuned to solve the main or "downstream task". The literature on self-supervision for medical imaging applications is still scarce [74][75][76][77], but for instance, a recent work used context restoration as a pretext task [76]. Especifically, small patches in the image were randomly selected and swapped to obtain a new image with altered spatial information, and the pretext task consisted in predicting or restoring the original version of the image.…”

Section: Learning Style Common Algorithms / Methods Examplesmentioning

confidence: 99%

Artificial intelligence and machine learning for medical imaging: A technology review

Montero¹,

Javaid²,

Valdés

et al. 2021

Physica Medica

223

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Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence of disruptive technical advances and impressive experimental results, notably in the field of image analysis and processing. In medicine, specialties where images are central, like radiology, pathology or oncology, have seized the opportunity and considerable efforts in research and development have been deployed to transfer the potential of AI to clinical applications. With AI becoming a more mainstream tool for typical medical imaging analysis tasks, such as diagnosis, segmentation, or classification, the key for a safe and efficient use of clinical AI applications relies, in part, on informed practitioners. The aim of this review is to present the basic technological pillars of AI, together with the state-of-the-art machine learning methods and their application to medical imaging. In addition, we discuss the new trends and future research directions. This will help the reader to understand how AI methods are now becoming an ubiquitous tool in any medical image analysis workflow and pave the way for the clinical implementation of AI-based solutions.

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Section: Learning Style Common Algorithms / Methods Examplesmentioning

confidence: 99%

Artificial intelligence and machine learning for medical imaging: A technology review

Montero¹,

Javaid²,

Valdés

et al. 2021

Physica Medica

223

View full text Add to dashboard Cite

show abstract

“…Rubik cube+ was evaluated on the same down-stream tasks from the previous work which showed slight improvement. Nguyen et al [2020] proposed spatial awareness pretext task that is able to learn semantic and spatial representation from volumetric medical images. Spatial awareness is inspired in the context restoration framework but was treated as a classification problem.…”

Section: Predictive Methods In Medical Imagingmentioning

confidence: 99%

“…Self-supervised learning became a popular choice in the filed of medical image analysis where the amount of the available annotated data is relatively small while the unlabeled data is comparatively large. Several researches have demonstrated the effectiveness of the self-supervised learning approach throughout various medical images analysis tasks such as detection and classification [Lu et al, 2020, Sriram et al, 2021, detection and localization , Sowrirajan et al, 2021, Nguyen et al, 2020, and segmentation tasks [Taleb et al, 2020, Chaitanya et al, 2020.…”

Section: Introductionmentioning

confidence: 99%

Self-supervised learning methods and applications in medical imaging analysis: A survey

Shurrab¹,

Duwairi²

2021

Preprint

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The availability of high quality annotated medical imaging datasets is a major problem that collides with machine learning applications in the field of medical imaging analysis and impedes its advancement. Self-supervised learning is a recent training paradigm that enables learning robust representations without the need for human annotation which can be considered as an effective solution for the scarcity in annotated medical data. This article reviews the state-of-the-art research directions in self-supervised learning approaches for image data with concentration on their applications in the field of medical imaging analysis. The article covers a set of the most recent self-supervised learning methods from the computer vision field as they are applicable to the medical imaging analysis and categorize them as predictive, generative and contrastive approaches. Moreover, the article covers (40) of the most recent researches in the field of self-supervised learning in medical imaging analysis aiming at shedding the light on the recent innovation in the field. Ultimately, the article concludes with possible future research directions in the field.

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“…With focus on volumetric anatomy segmentation, Chaitanya et al [5] introduce a framework for self-supervised learning based on a hybrid contrastive loss, that learns both global and local image representations. For the same application, Nguyen et al [6] propose to use spatial awareness as signal for self-supervised learning -learning to predict the displacement of different image slices after random swaps of image patches between slices. Finally, Azizi et al [39] rely on the contrastive learning based method proposed in [2] to pretrain features and improve the accuracy of various downstream classification tasks from radiography or dermatology images.…”

Section: Self-supervised Learning In the Medical Domainmentioning

confidence: 99%

“…Only few studies have investigated the impact of self-supervised learning in the medical image analysis domain (e.g., [4]- [6]) -a field where the development of AI technologies is impacted by a high cost of annotations (often requiring expert radiologists precision) and scarcity of medical imaging data. These solutions are generally limited in their design to focus on architectures for segmentation (i.e., encoder-decoder) and do not support deep architectures often used for classification or detection [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Self-supervised Learning from 100 Million Medical Images

Ghesu¹,

Georgescu²,

Mansoor³

et al. 2022

Preprint

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Building accurate and robust artificial intelligence systems for medical image assessment requires not only the research and design of advanced deep learning models but also the creation of large and curated sets of annotated training examples. Constructing such datasets, however, is often very costly -due to the complex nature of annotation tasks and the high level of expertise required for the interpretation of medical images (e.g., expert radiologists). To counter this limitation, we propose a method for self-supervised learning of rich image features based on contrastive learning and online feature clustering. For this purpose we leverage large training datasets of over 100,000,000 medical images of various modalities, including radiography, computed tomography (CT), magnetic resonance (MR) imaging and ultrasonography. We propose to use these features to guide model training in supervised and hybrid self-supervised/supervised regime on various downstream tasks. We highlight a number of advantages of this strategy on challenging image assessment problems in radiography, CT and MR: 1) Significant increase in accuracy compared to the state-of-theart (e.g., AUC boost of 3-7% for detection of abnormalities from chest radiography scans and hemorrhage detection on brain CT); 2) Acceleration of model convergence during training by up to 85% compared to using no pretraining (e.g., 83% when training a model for detection of brain metastases in MR scans); 3) Increase in robustness to various image augmentations, such as intensity variations, rotations or scaling reflective of data variation seen in the field.

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Self-Supervised Learning Based on Spatial Awareness for Medical Image Analysis

Cited by 37 publications

References 32 publications

Artificial intelligence and machine learning for medical imaging: A technology review

Artificial intelligence and machine learning for medical imaging: A technology review

Self-supervised learning methods and applications in medical imaging analysis: A survey

Self-supervised Learning from 100 Million Medical Images

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