PMData

Thambawita, Vajira; Hicks, Steven A.; Borgli, Hanna; Stensland, Håkon Kvale; Jha, Debesh; Svensen, Martin Kristoffer; Pettersen, Svein-Arne; Johansen, Dag; Johansen, Håvard D.; Pettersen, Susann Dahl; Nordvang, Simon; Pedersen, Sigurd; Gjerdrum, Anders Tungeland; Grønli, Tor‐Morten; Fredriksen, Per Morten; Eg, Ragnhild; Hansen, Kjeld; Fagernes, Siri; Claudi, Christine; Biørn-Hansen, Andreas; Nguyen, Duc Tien Dang; Kupka, Tomas; Hammer, Hugo Lewi; Jain, Ramesh; Riegler, Michael; Halvorsen, Pål

doi:10.1145/3339825.3394926

Cited by 33 publications

(15 citation statements)

References 13 publications

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“…However, we soon realized a huge lack of medical data to develop good ML models in the domain for various reasons, increasing the importance of the first two steps in Figure 1 Therefore, we have studied how datasets should be collected, composed, and published as open datasets. Within the three years of Ph.D. time, a total of seven datasets [23,24,25,26,27,28,29] were successfully collected and published. In these datasets, medical experts labeled or annotated data (Step II), but not all the datasets because the annotation process is costly and time-consuming.…”

Section: Background and Motivationmentioning

confidence: 99%

“…For the cardiology branch, an ML-based ECG analysis system [41] was researched and implemented. Moreover, all the dataset papers [23,24,25,26,27,28,29] introduced ML models as baseline experiments which can be considered initial models for developing CAD systems.…”

Section: Contributionsmentioning

confidence: 99%

“…Moreover, these datasets are the main assets for initiating the novel concept and the corresponding framework, DeepSynthBody, introduced in this thesis. Different types of real medical datasets [23,24,25,26,27,28,29] were collected and published to the research community with the baseline experiments under this thesis to accomplish the sub-objective I,. All the datasets contribute to designing ML models for CAD systems (sub-objective I) because of the baseline experiments introduced in every dataset paper.…”

Section: Contributionsmentioning

confidence: 99%

“…Therefore, collecting medical data is identified as a key step for the thesis. As a result, several datasets [23,24,25,26,27,28,29] were published. More details about these datasets are discussed in Section 3.1.1.…”

Section: Medical Datamentioning

confidence: 99%

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DeepSynthBody: the beginning of the end for data deficiency in medicine

Thambawita

Hicks

Isaksen

et al. 2021

2021 International Conference on Applied Artificial Intelligence (ICAPAI)

Self Cite

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Hammer, for their support, motivation, and always behind me. Without them, this would be only a dream. After joining the HOST department as a Ph.D. candidate in 2018, I started experiencing a completely new environment with new people from different countries and cultures.Michael Riegler became my principal supervisor. I did not know anything about him despite his academic background. However, after few weeks, I realized that he is more than my principal supervisor for my life. Within few months, he became a game-changer in my life. I do not have words to express his qualities and how his advice, encouragement, kindness, and motivations are important to my life. Therefore, I would like to give my most enormous thanks to my primary supervisor, Michael, who is always behind me to support my academic life and get advice for my personal life.Pål Halvorsen is the department head and one of my co-supervisors, and he is a very kind person supporting us always silently. His advice and encouragements make me better and better every day in my academic life. Then, I would like to thank Pål for his kindness showed me through my Ph.D. journey and the wordless help given to me. Hugo Hammer is my second co-supervisor who supports me in handling advanced statistical problems in my Ph.D. research. Then, I would like to thank Hugo for his friendly help, as always.The research has benefited from the Experimental Infrastructure for Exploration of Exascale Computing (eX3), which the Research Council of Norway financially supports under contract 270053. Tore was the person who helped me to use these infrastructures.So, I would like to give my special thanks to him for his outstanding support.I also would like to thank the closest colleague, Steven Hicks, a good friend met within the department and in my life. Moreover, I thank all the other colleagues, Debesh, Hanna, Pia, Hakon, and Daniel, who work closely in my Ph.D. periods. There are more people v I want to thank, but writing them all here is not possible. However, they all are equally important for me, and therefore I would like to thank them all equally. Master students, other Ph.D. students from different departments and other counties, all the co-authors, and persons who are from the administration are a few of them.Finally, I would like to thank my wife, Shalike, who sacrificed the freedom of her life for finishing my Ph.D. successfully. Not only that, she always provided me food and other necessary stuff without asking anything from me. Last but not least, I would like to give my thanks to my parents, who sacrificed their whole life to provide us a good life while facing a challenging lifestyle in my country,

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Section: Background and Motivationmentioning

confidence: 99%

Section: Contributionsmentioning

confidence: 99%

Section: Contributionsmentioning

confidence: 99%

Section: Medical Datamentioning

confidence: 99%

See 2 more Smart Citations

DeepSynthBody: the beginning of the end for data deficiency in medicine

Thambawita

Hicks

Isaksen

et al. 2021

2021 International Conference on Applied Artificial Intelligence (ICAPAI)

Self Cite

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“…Transferring these tasks to the strength train- feedback [509,804], examples of traditional loggers are Gravitus R (https://gravitus.com/), Strong R (https://www.strong.app/), gymaholic R (http://www.gymaholic.me/). These consumerlevel train loggers do not typically handle aggregation of objective measures (see section III later), and their use for advanced data-driven training modeling is somewhat limited.…”

Section: Precision Strength Trainingmentioning

confidence: 99%

Precision strength training: Data-driven artificial intelligence approach to strength and conditioning

Teikari¹,

Pietrusz²

2021

Preprint

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In strength training, personalised strength training (autoregulation) approaches have been used to individualise exercise programs with monitoring an for dynamic adjustment based on their responses to training. While this transition from tradition-based training to evidence-based training framework has been an improvement in training practices, we argue that the future of strength training will also incorporate deep learning models powered by data. We refer to this data-driven framework as precision strength training inspired by the similar modeling frameworks used in precision medicine. In contrast to current personalised training in which the acquired athlete data is often subject to human expert decision-making, we are anticipating the rise of human-in-the-loop systems with an augmented coach who will be doing decisions collaboratively with the machine. Similar to other precision frameworks, such as precision health, we envision such a future to take decades to be realised and we focus here on practical short-term targets on a way to long-term realisation. In this chapter, we will review the measurement technology needed for continuous data acquisition from an individual during training/physical activity, how to acquire these datasets for the development of such systems and, how a proof-of-concept system could be developed for powerlifting training with applicability to general strength and conditioning (S&C) and physical rehabilitation purposes. Additionally, we will evaluate how the user experience (UX) of the system feedback and visualisation could be designed.

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