Object detection methods have been developed since early 2000s and continue to grow rapidly until now. The history of object detection can be separated into two eras: traditional detection methods and deep learning based detection methods.
In the last decade the application of artificial intelligence (AI) algorithms in dermatology to classify skin lesions, particularly melanoma, has advanced rapidly. Large international computer skin image analysis challenges have successfully drawn attention to the potential for AI to aid the detection of skin cancers.
Object detection is a computer vision technology that can detect objects in images and videos. It answers the question: which object is presented and where is it?
In recent times, data has become one of the most precious resources in both business and science. For projects such as iToBoS, which aims to utilize deep learning in the global fight against melanoma, the veracity, validity and volume of data is essential.
Due to the ubiquity of AI systems in our society, awareness has been raised for the need of neural networks and their predictions to be transparent and explainable.
DICOM (Digital Imaging and Communications in Medicine) files are a standard for storing and transmitting medical imaging information. They are more commonly used in various radiology modalities, such as MRI or CT scans, as they can contain multiple frames of an image in a single file, thus allowing to store a 3D image formed by slices.
Recent advances in software and hardware technologies have revolutionized data-driven research projects such as iToBoS by greatly accelerating the execution of computational tasks.
Skin cancer is the most common type of cancer, and early detection is crucial for successful treatment. One approach to detecting skin cancer is to use change detection, where changes in the skin over time are analyzed to identify potential malignancies.
Histologic analysis of melanocytic lesions can be supported by immunohistochemistry and in research also by in situ hybridization (ISH).
The first Periodic Reporting corresponding to the period M1-M18 was submitted to the EC on November 24th, 2022 (M20) and it was officially approved on January 25th, 2023.