Decentralised Screening in Dermatology


Mobile technology for healthcare professionals, validated as monitoring or referral solution for skin lesions.



One in every three cancers diagnosed worldwide is a skin cancer, and nearly three million new cases of skin cancer are detected annually. Smartphones are well suited to maximise the accessibility of solutions for early detection of pathologies, particularly in dermatology where image acquisition plays such a crucial role.



AICOS has been performing research in Mobile Dermatology since 2011, developing a solution to improve the referral process for skin lesions between Local Health Centres (LHC) and Hospital Dermatology Departments (HDD).

With our solution, general practitioners from LHC are able to acquire relevant dermatological data with a mobile application, in an easy and intuitive way. One of the main differentiation factor of our solution is the automatic image acquisition software, that autonomously controls the image quality through the real-time computation of relevant focus metrics and control of camera parameters, and also segments the skin mole in real-time for further analysis.

This solution also aims to assist the dermatologists from HDD in the prioritisation of cases, through a decision support system that automatically performs risk assessment of mobile-acquired skin lesion images. A machine learning algorithm was developed based on image processing that processes and extracts significant features based on the ABCD rule of dermatoscopy. It was developed using a dataset of nearly 430 pigmented skin lesions, independently annotated by five dermatologists.



Our solution improves the efficiency of the referral process by: i) facilitating the acquisition of relevant dermatological data by non-specialists; ii) ensuring image quality; and iii) providing a decision support system for cases prioritisation to help dermatologists. Besides this, our technology is also being developed to be used in other types of skin lesions such as skin ulcers.

Highlighted Projects



This project aims to improve the existing Teledermatology processes between Primary Care Units and Dermatology Services in the National Health Service for skin lesions diagnosis through the usage of Artificial Intelligence.



The ClinicalWoundSupport project has as main objective the investigation, development and validation of a management support and support to clinical decision / action solution for wound monitoring and treatment.

several clinical contexts.



A framework of risk triage of skin cancer, which uses a new generation of mobile
devices in its architecture to capture the

Further information




Derma Brochure


Relevant Services


Rapid Prototyping

Innovation Studies 

Education & Training 


Relevant Publications


Andrade, C.; Teixeira, L.; Vasconcelos, M. J. M.; & Rosado, L. (2021). Data Augmentation using Adversarial Image-to-Image Translation for the Segmentation of Mobile-acquired Dermatological Images. Journal of Imaging 7(1), 2. DOI: 10.3390/jimaging7010002. More info

Carvalho, R.; Morgado, A.C.; Andrade, C.; Nedelcu, T.; Carreiro, A.; & Vasconcelos, M. J. M. (2021). Integrating Domain Knowledge into Deep Learning for Skin Lesions Risk Prioritization to assist Teledermatology Referral. Diagnostics, 12(1), 36. DOI: 10.3390/diagnostics12010036. More info

Moreira, D.; Alves, P.; Veiga, F.; Rosado, L.; & Vasconcelos, M. J. M. (2021). Automated Mobile Image Acquisition of Macroscopic Dermatological Lesions. Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5, 122-132, HEALTHINF, February 11-13, Online. More info

Morgado, A. C.; Andrade, C.; Teixeira, L.; & Vasconcelos, M. J. M. (2021). Incremental Learning for Dermatological Imaging Modality Classification. Journal of Imaging 2021, 7(9), 180. DOI: 10.3390/jimaging7090180. More info

Nedelcu, T.; Carreiro, A.; Veiga, F.; & Vasconcelos, M. J. M. (2021). Challenges on Real-World Skin Lesion Classification: Comparing Fine-tuning Strategies for Domain Adaptation using Deep Learning. The Sixth International Conference on Informatics and Assistive Technologies for Health-Care, Medical Support and Wellbeing (HEALTHINFO 2021), October 3-7, Barcelona, Spain, IARIA, 23-31. DOI: 10.1007/978-3-030-90439-5_3. More info

Alves J., Moreira D., Alves P., Rosado L., & Vasconcelos M.J.M. (2019). Automatic Focus Assessment on Dermoscopic Images Acquired with Smartphones. Sensors 2019, 19, 4957. More info

Faria, J., Almeida, J., Vasconcelos, M. J. M., & Rosado, L. (2019). Automated mobile image acquisition of skin wounds using real-time deep neural networks. In Annual Conference on Medical Image Understanding and Analysis (pp. 61-73). Springer, Cham. More info 

Rosado L., & Vasconcelos M.J.M. (2015). Automatic Segmentation Methodology for Dermatological Images Acquired via Mobile Devices. In Proceedings of International Conference on Health Informatics - Volume 1: HEALTHINF , 246-251. More info

Rosado L., Vasconcelos M.J.M., Castro R., & Tavares J.M.R.S. (2015). From Dermoscopy to Mobile Teledermatology. In Dermoscopy Image Analysis, 385-418. More info

Rosado L., Vasconcelos M.J.M., Correia F., & Costa N. (2015). A Novel Framework for Supervised Mobile Assessment and Risk Triage of Skin Lesions. In Proceedings of the 9th International Conference on Pervasive Computing Technologies for Healthcare, 266-267.
More info

Vasconcelos M.J.M., Rosado L., & Ferreira M. (2014). Principal Axes-based Asymmetry Assessment Methodology for Skin Lesion Image Analysis. In ISVC 2014: Advances in Visual Computing, 21-31. More info

Rosado L., Vasconcelos M.J.M., & Ferreira M. (2013). A Mobile-Based Prototype for Skin Lesion Analysis: Towards a Patient-Oriented Design Approach. In International Journal of Online Engineering (iJOE), 9(8), 27-29. More info