Melanoma is responsible for 60% of fatal skin neoplasia; its high capacity to metastasize makes it one of the most aggressive cancers.
The increase in life expectancy expands its incidence and makes this cancer a future public health challenge. According to the latest statistics, cutaneous melanoma is currently the sixth most common type of cancer in Europe, with over 144,000 new cases diagnosed in 2018.
Fortunately, melanoma can be cured if treated at an early stage. More than 90% of melanoma patients are still alive after 5 years, if treated at an early stage.
For these reasons, early diagnosis is essential to ensure treatment before local and metastatic spread. The comprehensive skin examination, the primary screening mechanism for melanoma, checks each pigmented skin lesion individually for typical signs of melanoma. This technique can be time consuming for patients with atypical mole syndrome or a large number of nevi.
The iToBoS (Intelligent Total Body Scanner for Early Detection of Melanoma) project aims to train an artificial intelligence (AI) system capable of integrating information from different sources, ranging from dermoscopic images and comprehensive medical records to genomics.
The iToBoS project is developing a platform that includes a novel total body scanner and a computer-aided diagnosis (CAD) tool to integrate various data sources such as medical records, genomic data, and in vivo imaging.
The proposed holistic approach will enable physicians to diagnose skin diseases earlier and with greater accuracy, increasing the effectiveness and efficiency of personalized clinical decisions.
The consortium, which includes 19 partner organizations, is led by the University of Girona (Spain).This international consortium brings together leading academic and research institutions (5 research centers) research centers), industries (4 companies and 6 SMEs) and end-user entities (3 hospitals and 1 patient NGO). OF PATIENTS): University of Girona (Spain), Optotune Suisse AG (Switzerland), IBM Israel-Science and technology Ltd (Israel), Robert Bosch España Fábrica Madrid SA (Spain), Barco NV (Italy), National Technical University of Athens-NTUA (Greece), Gottfried Wilhelm Leibniz Universitaet Hannover (Germany), Fundació Clinic per a la Recerca Biomédica (Spain), Ricoh Spain IT Services SLU (Spain), Trilateral Research Limited (Ireland) Universita degli Studi di Trieste (Italy), Coronis Computing SL (Spain), Torus Actions (FR), V7 LTD (UK), ISAHIT (France), The University of Queensland (Australia), Szamitastechnikai es Automatizalasi Kutatointezet (Hungary), Fraunhofer Gesellschaft zur Foerderung der Angewandten Forschung E.V. (Germany), Melanoma Patient Network Europe (Sweden).
Our role in the project is to annotate the dataset of melanomas and skin lesions recovered by the scanner, so as to train the AI model to recognize lesions.
We have a large community of annotators, over 1000 women currently work with us and can annotate a large amount of data.
The diversity of our annotators (who live in 37 different countries) is a great strength for this project because they can spot and recognize melanomas on different skin types.
For more than 5 years, we have been offering our labeling services to our clients and year after year we gain precision and expertise. We have performed more than 3,000,000 tasks for our clients (+ 350).
We have completed more than 500 computer vision projects and have a unique annotation tool that adapts to the different projects of our clients, their model and their budget. Today, we offer all types of annotation types : 2D image annotation, 2 bounding box, 3 bounding box box, polygons, semantic segmentation, points, graph, polylines, ocr transcription, classification, lidar.
⚕️ We are very proud to be part of this project and to contribute to making AI a real support for science and medicine!
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