Conference Paper

Automated Mobile Image Acquisition of Macroscopic Dermatological Lesions

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... With ongoing advancements, these devices have seen increases in both versatility and performance, alongside enhancements in the number of their embedded sensors. These improvements open new avenues for utilizing mobile devices in specialized image processing tasks in various contexts, including industrial applications [3], agriculture [4] and healthcare [5]. ...
... This approach can significantly enhance image quality and improve object detection accuracy in mobile applications. [5], [15]. ...
... A method for automated image focus assessment is proposed in [5] for segmenting dermatological lesions using a mobile application. The results demonstrated that the method not only effectively distinguishes focused from non-focused images but also enables real-time processing and provides user feedback. ...
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... The present work integrates a larger project, DermAI, that aims to improve the existing Teledermatology processes between Primary Care Units (PCU) and Dermatology Services in the Portuguese National Health Service (NHS) for skin lesion referral. Through the usage of Artificial Intelligence (AI) and Computer Vision, we envision two major goals: (a) to support doctors in Primary Care Units through the development of a mobile application that fosters image acquisition standardization [9] and (b) to assist dermatologists in the referral process for booking specialist consultations in the hospital through the adequate prioritization of cases. Improving dermatology consultations' prioritization is particularly relevant in the Portuguese scenario due to the lack of specialists in the NHS and the long waiting lists for this type of consultation. ...
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Teledermatology has developed rapidly in recent years and is nowadays an essential tool for early diagnosis. In this work, we aim to improve existing Teledermatology processes for skin lesion diagnosis by developing a deep learning approach for risk prioritization with a dataset of retrospective data from referral requests of the Portuguese National Health System. Given the high complexity of this task, we propose a new prioritization pipeline guided and inspired by domain knowledge. We explored automatic lesion segmentation and tested different learning schemes, namely hierarchical classification and curriculum learning approaches, optionally including additional patient metadata. The final priority level prediction can then be obtained by combining predicted diagnosis and a baseline priority level accounting for explicit expert knowledge. In both the differential diagnosis and prioritization branches, lesion segmentation with 30% tolerance for contextual information was shown to improve classification when compared with a flat baseline model trained on original images; furthermore, the addition of patient information was not beneficial for most experiments. Curriculum learning delivered better results than a flat or hierarchical approach. The combination of diagnosis information and a knowledge map, created in collaboration with dermatologists, together with the priority achieved interesting results (best macro F1 of 43.93% for a validated test set), paving the way for new data-centric and knowledge-driven approaches.
... Digital cameras and cameras embedded in mobile devices increase the quality and resolution of the images captured [1,2], allowing for powerful solutions for different areas of human life [3,4]. Currently, the emergence of studies related to healthcare and technology is increasing, and it is participating in the new era of medicine related to patient empowerment [5]. ...
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... In summary, the detailed control of image quality and adequacy should be considered an extremely important factor during the design of a mobile application intended for trapbased insect monitoring. In fact, promising results have been recently reported for different healthcare solutions [22][23][24], by embedding AI to effectively support the user in the handheld image acquisition process. However, the use of similar approaches in viticulture is non-existent. ...
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Every year, the number of skin cancer cases has been increasing which, consequently, increases the strain on the health care systems around the globe. With the growth of processing power and camera quality on smartphones, the investment in telemedicine and the development of mobile teledermatology applications can, not only contribute to the standardization of image acquisitions but also, facilitate early diagnosis. This paper presents a new process for real-time automated image acquisition of macroscopic skin images with the merging of an automated focus assessment feature-based machine learning algorithm with conventional computer vision techniques to segment dermatological images. Three datasets were used to develop and evaluate the proposed methodology. One comprised of 3428 images acquired with a mobile phone for this purpose and 1380 from the other two datasets which are publicly available. The best focus assessment model achieved an accuracy of 88.3% and an F1-Score of 86.8%. The segmentation algorithm obtained a Jaccard index of 85.81% for the SMARTSKINS dataset and 68.59% for the Dermofit dataset. The algorithms were deployed to a mobile application, available in Android and iOS, without causing any performance hindrances. The application was tested in a real environment, being used in a 10-month pilot study with six General and Family Medicine doctors and one Dermatologist. The easiness to acquire dermatological images, image quality, and standardization were referred to as the main advantages of the application.
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