# The Skincare project, an interactive deep learning system for differential diagnosis of malignant skin lesions. Technical Report

arXiv: https://arxiv.org/abs/2005.09448 · HTML: https://mlsanity.com/paper/2005.09448/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2020.

## Built on by (most cited first, 6 of 6)

- [ExAID: A Multimodal Explanation Framework for Computer-Aided Diagnosis of Skin Lesions](https://mlsanity.com/paper/2201.01249/) (2022)
- [Self-Supervised Domain Adaptation for Diabetic Retinopathy Grading using Vessel Image Reconstruction](https://mlsanity.com/paper/2107.09372/) (2021)
- [DRG-Net: Interactive Joint Learning of Multi-lesion Segmentation and Classification for Diabetic Retinopathy Grading](https://mlsanity.com/paper/2212.14615/) (2022)
- [Deep Learning Based Decision Support for Medicine -- A Case Study on Skin Cancer Diagnosis](https://mlsanity.com/paper/2103.05112/) (2021)
- [A look under the hood of the Interactive Deep Learning Enterprise (No-IDLE)](https://mlsanity.com/paper/2406.19054/) (2024)
- [Fine-tuning of explainable CNNs for skin lesion classification based on dermatologists' feedback towards increasing trust](https://mlsanity.com/paper/2304.01399/) (2023)

## Abstract (arXiv)

A shortage of dermatologists causes long wait times for patients who seek dermatologic care. In addition, the diagnostic accuracy of general practitioners has been reported to be lower than the accuracy of artificial intelligence software. This article describes the Skincare project (H2020, EIT Digital). Contributions include enabling technology for clinical decision support based on interactive machine learning (IML), a reference architecture towards a Digital European Healthcare Infrastructure (also cf. EIT MCPS), technical components for aggregating digitised patient information, and the integration of decision support technology into clinical test-bed environments. However, the main contribution is a diagnostic and decision support system in dermatology for patients and doctors, an interactive deep learning system for differential diagnosis of malignant skin lesions. In this article, we describe its functionalities and the user interfaces to facilitate machine learning from human input. The baseline deep learning system, which delivers state-of-the-art results and the potential to augment general practitioners and even dermatologists, was developed and validated using de-identified cases from a dermatology image data base (ISIC), which has about 20000 cases for development and validation, provided by board-certified dermatologists defining the reference standard for every case. ISIC allows for differential diagnosis, a ranked list of eight diagnoses, that is used to plan treatments in the common setting of diagnostic ambiguity. We give an overall description of the outcome of the Skincare project, and we focus on the steps to support communication and coordination between humans and machine in IML. This is an integral part of the development of future cognitive assistants in the medical domain, and we describe the necessary intelligent user interfaces.