Fig. 1. Schematics of typical application scenarios of intelligence teleconsultation system for skin tumors built by our team. (a) Skin tumor self-screening; (b) tele-consultation

Fig. 2. Intelligence teleconsultation system for skin tumors. (a) System hardware composition; (b) schematic of AI-CPI module; (c) schematic of principle

Fig. 3. Software design of intelligence teleconsultation system for skin tumors. (a) User interface; (b) workflow under network condition

Fig. 4. Quantification experiment of projection accuracy of AI-CPI module. (a) Schematic of experiment; (b) projection errors at different working distances; (c) projection errors at different working angles

Fig. 5. Quantification experiment of resolution and color accuracy of AI-CPI module. (a) USAF 1951 resolution target plate captured by camera; (b) re-projected target plate; (c) changes in intensity of two regions of interest (ROI) along dotted line; (d) X-rite color card images taken by camera; (e) color card images after color calibration; (f) CIEDE2000 histogram before and after color correction

Fig. 6. Teleconsultation experiment of skin tumor using proposed system. (a) Subject and system at local site; (b) experienced expert is viewing images of skin tumor; (c) deep learning algorithm deployed in system diagnoses lesion as basal cell carcinoma through dermoscopic images; (d) expert confirms that diagnosis result of algorithm is correct, and draws annotations to discuss surgical plan; annotations projected on subject skin surface by proposed system are used to indicate (e) area of tissue removed and (f) area of skin flap reconstruction