Graphical User Interface for Medical Deep Learning - Application to Magnetic Resonance Imaging

Milde, Sebastian; Liebgott, Annika; Wu, Ziwei; Feng, Wei; Yang, Jiahuan; Mauch, Lukas; Martirosian, Petros; Bamberg, Fabian; Nikolaou, Konstantin; Gatidis, Sergios; Schick, Fritz; Yang, Bin; Küstner, Thomas

doi:10.23919/apsipa.2018.8659515

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…If an image has one or more contours associated with it, the same transformation is applied to the contours. Geometric transformations are so common that they were utilised by 92 of the 93 basic augmentation studies 15–106 …”

Section: Methodsmentioning

confidence: 99%

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A review of medical image data augmentation techniques for deep learning applications

et al. 2021

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Summary Research in artificial intelligence for radiology and radiotherapy has recently become increasingly reliant on the use of deep learning‐based algorithms. While the performance of the models which these algorithms produce can significantly outperform more traditional machine learning methods, they do rely on larger datasets being available for training. To address this issue, data augmentation has become a popular method for increasing the size of a training dataset, particularly in fields where large datasets aren’t typically available, which is often the case when working with medical images. Data augmentation aims to generate additional data which is used to train the model and has been shown to improve performance when validated on a separate unseen dataset. This approach has become commonplace so to help understand the types of data augmentation techniques used in state‐of‐the‐art deep learning models, we conducted a systematic review of the literature where data augmentation was utilised on medical images (limited to CT and MRI) to train a deep learning model. Articles were categorised into basic, deformable, deep learning or other data augmentation techniques. As artificial intelligence models trained using augmented data make their way into the clinic, this review aims to give an insight to these techniques and confidence in the validity of the models produced.

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Section: Methodsmentioning

confidence: 99%

“…Gamma correction (3j), 107 linear contrast (3k) and histogram equalisation (3l) are common methods to adjust the contrast of an image. Twenty‐eight studies utilised intensity operations for data augmentation 18,20,21,28,34,38,40–42,45,46,55,62,67,70,75–77,79,84,87,88,94,102–104,106,108 …”

Section: Methodsmentioning

confidence: 99%

A review of medical image data augmentation techniques for deep learning applications

et al. 2021

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“…Whether it involves tasks like image recognition, natural language understanding, or predictive analytics, these tools empower users to integrate Deep Learning capabilities into their workflows effortlessly. For instance, such a tool could enable users to input data and receive predictions or classifications generated by the model's outputs, all without requiring them to possess an extensive grasp of the intricate mathematical details and algorithms that power the model (Milde et al, 2018;Yaacoub et al, 2022). Furthermore, these interaction tools assume a pivotal role in enabling realtime engagement with Deep Learning models, a critical aspect in applications like autonomous vehicles or emergency response systems, mirroring the context of our thesis work.…”

Section: Introductionmentioning

confidence: 99%

Deeep Continual Multimodal Multitask Modells for Out-of-Hospital Emergency Medical Call Incidents Triage Support in the Presence of Dataset Shifts

Ferri Borredà

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Adoken: Deep Learning Based Decision Support Software for Mri

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Okyay

Adar

2021

Mühendislik Bilimleri Ve Tasarım Dergisi

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Makine öğrenmesinin alt sınıfı olan derin öğrenme, birden çok katman ile ham veriden özelliklerin çıkarılmasını sağlamaktadır. Son yıllardaki teknolojik gelişmeler ile özellikle sağlık alanındaki görüntü işleme çalışmalarında sıklıkla tercih edilmektedir. Başarılı sonuçlar elde etmek için derin öğrenme modellerindeki parametrelerin optimize edilmesi gerekir. Bu işlemin belli bir düzeyde yazılım bilgisi gerektirmesi, alana yeterince hâkim olmayan kişilere zorluk oluşturabilmektedir. Araştırmacılar, kodlama gerektirmemesi nedeniyle hazır derin öğrenme modellerini ve görsel araçları tercih edebilmektedirler. Bu çalışmada önerilen uygulama aracılığıyla, manyetik rezonans görüntüleme taramaları için kompleks derin öğrenme işlemlerinin doğrudan grafik arayüzü üzerinden gerçekleştirilmesi hedeflenmektedir. Uygulama; veri seçimi, ön işleme, model oluşturma, eğitim ve test ana modüllerinden oluşmaktadır. Önde gelen bazı derin öğrenme modelleri uygulamaya entegre edilmiş olarak sunulmaktadır. İzlenen uyumluluk tasarımı sayesinde gelecekte yeni mimarilerin de kolaylıkla eklenebilmesinin önü açılmıştır. Modüller, açık kaynak manyetik rezonans görüntüleme verisi aracılığıyla doğrulanarak uygulamanın test tabanlı geliştirilmesi sağlanmıştır. Fonksiyonellik doğrulama testlerinde üç boyutlu evrişimsel sinir ağı kullanılarak literatüre paralel şekilde %81 doğruluk oranı gözlemlenmiştir. Uygulamanın radyoloji uzmanları ve araştırmacılar gibi kullanıcılar tarafından karar destek amacıyla kullanılabileceği düşünülmektedir.

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Graphical User Interface for Medical Deep Learning - Application to Magnetic Resonance Imaging

Cited by 3 publications

References 16 publications

A review of medical image data augmentation techniques for deep learning applications

A review of medical image data augmentation techniques for deep learning applications

Deeep Continual Multimodal Multitask Modells for Out-of-Hospital Emergency Medical Call Incidents Triage Support in the Presence of Dataset Shifts

Adoken: Deep Learning Based Decision Support Software for Mri

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