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.
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.
In this video we show why the researchers at Leibniz University Hannover use polarized light for imaging of the human skin. The cross-polarization filters specular reflections at the uppermost skin layer and enables the team to look deeper into the skin. This is of importance for the early detection of melanoma.
Each one of the iToBoS full-body scanners features 15 cameras that will take pictures of different regions of the patient’s body. To cover these regions with high resolution, the cameras will be distributed in 5 arches and will scan their region thanks to several motors.