Existing skin attributes detection methods usually initialize with a pre-trained Imagenet network and then fine-tune on a medical target task. However, we argue that such approaches are suboptimal because medical datasets are largely different from ImageNet and often contain limited training samples.
In this work, we propose Task Agnostic Transfer Learning (TATL), a novel framework motivated by dermatologists’ behaviors in the skincare context. Our method learns an attribute-agnostic segmenter that detects lesion skin regions and then transfers this knowledge to a set of attribute-specific classifiers to detect each particular attribute. Since TATL’s attribute-agnostic segmenter only detects skin attribute regions, it makes use of ample data from all attributes, allows transferring knowledge among features, and compensates for the lack of training data from rare attributes. The empirical results show that TATL not only works well with multiple architectures but also can achieve state-of-the-art performances while enjoying minimal model and computational complexities (30-50 times less than the number of parameters). We also provide theoretical insights and explanations for why our transfer learning framework performs well in practice.
The figure below demonstrates the usefulness of TATL when predicted lesion skin regions (predicted union) could cover both large regions as in Pigment Network and small disconnected regions as in Negative Network.
Ophthalmo-AI: Virtual Kick-off of the Ophthalmo-AI project (BMBF) in Saarbrücken, Sulzbach, Münster, Berlin and Heidelberg. In Ophthalmo-AI we aim at developing better diagnostic and therapeutic decision support in ophthalmology through effective collaboration of machine and human expertise (Interactive Machine Learning – IML).
The German Standardization Roadmap Artificial Intelligence has been published recently in November 2020. In medicine, secure framework conditions have to be created; and the legal context, economy, technical aspects, acceptance, privacy, data security, and ethical aspects have to be taken into account.
In the pAItient project, where DFKI is responsible for secure framework conditions of AI systems, we have now published our first paper that explicitely addresses these aspects and standardisation needs of image preprocessing guidelines which are naturally subject to GDPR and DSGVO.
pAItient: Virtual Kick-Off of the pAItient project (BMG) in Saarbrücken and Heidelberg. In pAItient we aim at automating the development of AI-based approaches in medicine (from the idea to translation into cross-site clinical routine).
KI-Para-Mi: Kick-off of the KI-Para-Mi Project (BMBF) as webinar in Munich and Saarbrücken. In KI-Para-Mi, we develop an intelligent personnel planning system for flexible shift scheduling in nursing, which above all takes into account the interests of the employees.