TransparentAI: High-Accuracy and Explainable Melanoma Classification System
We adopt a user-centric approach, engaging in interdisciplinary research to understand and address the needs of dermatologists. By collaborating closely with medical professionals, we ensure that the system provides explanations they can trust and rely on for making informed decisions. This approach not only fosters trust in the algorithm but also ensures its practical utility in clinical settings.
- How can we integrate and optimize diverse data sources to create a comprehensive and high-quality dataset for melanoma classification?
This question addresses the challenge of aggregating and harmonizing data from various origins to enhance the model's training and performance.
-What methods can be employed to ensure the explainability and transparency of the melanoma classification algorithm, making it understandable and trustworthy for dermatologists?
This focuses on identifying techniques and frameworks that improve the interpretability of the model, ensuring that its decision-making process is clear and justifiable to medical professionals.
- How can we balance high-performance metrics (AUC and ROC) with the need for explainability and legal compliance in the development of the melanoma classification system?
This question explores the trade-offs and strategies for achieving optimal performance while adhering to regulatory requirements and maintaining the system's explainability.
Dr. Björn Friedrich, Carl von Ossietzky University, Oldenburg, Germany
Daniel Eckhoff, Ph.D., Huawei Research, Hongkong, Hongkong SAR
Prof. Dr. med. Ulrike Raap, University Hospital, Oldenburg, Germany
Prof. Dr.-Ing. Andreas Hein, Carl von Ossietzky University, Oldenburg, Germany