Generating Synthetic ECGs Using GANs for Anonymizing Healthcare Data

Piacentino, Esteban; Guarner, Alvaro; Ángulo, Cecilio

doi:10.3390/electronics10040389

Cited by 34 publications

(23 citation statements)

References 12 publications

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“…This idea dates back to the 1990's and is an active field of research that alleviates the cost and efforts needed to obtain and manually label real-world data. Nowadays, models (pre)trained on synthetic datasets have a broad range of utility including feature matching (DeTone et al, 2018) autonomous driving (Siam et al, 2021), robotics indoor and aerial navigation , scene segmentation (Roberts et al, 2021) and anonymized image generation in healthcare (Piacentino et al, 2021). The approaches broadly adopt the following process: pre-train with synthetic data before training on real-world scenes (DeTone et al, 2018;Hinterstoisser et al, 2019), generate composites of synthetic data and real images to create a new one that contains the desired representation (Hinterstoisser et al, 2018) or generate realistic datasets using simulation engines like Unity (Borkman et al, 2021) or generative models like GANs (Jeon et al, 2021;Mustikovela et al, 2021).…”

Section: Synthetic Data Generationmentioning

confidence: 99%

Knock, knock. Who's there? -- Identifying football player jersey numbers with synthetic data

Bhargavi¹,

Coyotl²,

Gholami³

2022

Preprint

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Automatic player identification is an essential and complex task in sports video analysis. Different strategies have been devised over the years, but identification based on jersey numbers is one of the most common approaches given its versatility and relative simplicity. However, automatic detection of jersey numbers is still challenging due to changing camera angles, low video resolution, small object size in wide-range shots and transient changes in the player's posture and movement. In this paper we present a novel approach for jersey number identification in a small, highly imbalanced dataset from the Seattle Seahawks practice videos. We use a multi-step strategy that enforces attention to a particular region of interest (player's torso), to identify jersey numbers. We generate in-house synthetic datasets of different complexities to supplement the data imbalance and scarcity in the samples. Our multi-step pipeline first identifies and crops players in a frame using a pretrained person detection model. We then utilize a pretrained human pose estimation model to localize jersey numbers (using torso key-points) in the detected players, obviating the need for annotating bounding boxes for number detection. This results in images that are on average 20x25px in size. We trained two light-weight Convolutional Neural Networks (CNNs) with different learning objectives: multi-class for two-digit number identification and multi-label for digit-wise detection to compare performance. Both models went through a pre-training round with the synthetic datasets and were finetuned with the real-world dataset to achieve a final best accuracy of 89%. Our results indicate that simple models can achieve an acceptable performance on the jersey number detection task and that synthetic data can improve the performance dramatically (accuracy increase of 9% overall, 18% on low frequency numbers) making our approach achieve state of the art results.

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Section: Synthetic Data Generationmentioning

confidence: 99%

Knock, knock. Who's there? -- Identifying football player jersey numbers with synthetic data

Bhargavi¹,

Coyotl²,

Gholami³

2022

Preprint

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“…To prevent data leakage by generating anonymized synthetic electrocardiograms (ECGs), Piacentino et al [132] used…”

Section: Medical and Healthcarementioning

confidence: 99%

A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines -- From Medical to Remote Sensing

Dash¹,

Ye²,

Wang³

2021

Preprint

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We look into Generative Adversarial Network (GAN), its prevalent variants and applications in a number of sectors. GANs combine two neural networks that compete against one another using zero-sum game theory, allowing them to create much crisper and discrete outputs. GANs can be used to perform image processing, video generation and prediction, among other computer vision applications.GANs can also be utilised for a variety of science-related activities, including protein engineering, astronomical data processing, remote sensing image dehazing, and crystal structure synthesis. Other notable fields where GANs have made gains include finance, marketing, fashion design, sports, and music. Therefore in this article we provide a comprehensive overview of the applications of GANs in a wide variety of disciplines. We first cover the theory supporting GAN, GAN variants, and the metrics to evaluate GANs.Then we present how GAN and its variants can be applied in twelve domains, ranging from STEM fields, such as astronomy and biology, to business fields, such as marketing and finance, and to arts, such as music. As a result, researchers from other fields may grasp how GANs work and apply them to their own study. To the best of our knowledge, this article provides the most comprehensive survey of GAN's applications in different field. CCS Concepts: • General and reference → Surveys and overviews; • Computing methodologies → Computer vision; Neural networks; • Theory of computation → Machine learning theory.

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“…In 2019, a research line was initiated [20][21][22] about using GANs for different medical fields. In particular it was demonstrated how data could be generated in the form of an image for databases like the Thyroid dataset, which is conformed by static data.…”

Section: Clinical-trial Data As An Imagementioning

confidence: 99%

Generative Adversarial Networks for Anonymized Healthcare of Lung Cancer Patients

et al. 2021

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The digital twin in health care is the dynamic digital representation of the patient’s anatomy and physiology through computational models which are continuously updated from clinical data. Furthermore, used in combination with machine learning technologies, it should help doctors in therapeutic path and in minimally invasive intervention procedures. Confidentiality of medical records is a very delicate issue, therefore some anonymization process is mandatory in order to maintain patients privacy. Moreover, data availability is very limited in some health domains like lung cancer treatment. Hence, generation of synthetic data conformed to real data would solve this issue. In this paper, the use of generative adversarial networks (GAN) for the generation of synthetic data of lung cancer patients is introduced as a tool to solve this problem in the form of anonymized synthetic patients. Generated synthetic patients are validated using both statistical methods, as well as by oncologists using the indirect mortality rate obtained for patients in different stages.

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Generating Synthetic ECGs Using GANs for Anonymizing Healthcare Data

Cited by 34 publications

References 12 publications

Knock, knock. Who's there? -- Identifying football player jersey numbers with synthetic data

Knock, knock. Who's there? -- Identifying football player jersey numbers with synthetic data

A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines -- From Medical to Remote Sensing

Generative Adversarial Networks for Anonymized Healthcare of Lung Cancer Patients

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