Melanoma is the deadliest form of skin cancer. Early diagnosis of malignant lesions is crucial for reducing mortality. The use of deep learning techniques on dermoscopic images can help in keeping track of the change over time in the appearance of the lesion, which is an important factor for detecting malignant lesions. In this paper, we present a deep learning architecture called Attention Squeeze U-Net for skin lesion area segmentation specifically designed for embedded devices. The main goal is to increase the patient empowerment through the adoption of deep learning algorithms that can run locally on smartphones or low cost embedded devices. This can be the basis to (1) create a history of the lesion, (2) reduce patient visits to the hospital, and (3) protect the privacy of the users. Quantitative results on publicly available data demonstrate that it is possible to achieve good segmentation results even with a compact model.
2022, JOURNAL OF DIGITAL IMAGING, Pages 1217-1230 (volume: 35)
Skin Lesion Area Segmentation Using Attention Squeeze U-Net for Embedded Devices (01a Articolo in rivista)
Pennisi Andrea, Bloisi Domenico D, Suriani Vincenzo, Nardi Daniele, Facchiano Antonio, Giampetruzzi Anna Rita
Gruppo di ricerca: Artificial Intelligence and Robotics