CLARIFY has significantly advanced the state of the art in a variety of aspects:
OPEN ENTERPRISE-SCALE INFRASTRUCTURE OF QUALITY-CONTROLLED WSIs
CLARIFY has investigated and built an advanced decentralized data flow management architecture for WSIs and related metadata, linking the necessary analysis tools in a federated cloud environment. The architecture is designed based on a computational notebook environment and can handle large, concentrated data streams from different sources to enable federated machine learning and privacy-preserved distributed workflows. The CLARIFY decentralized data management framework gives access to an open, comprehensive, and quality-controlled database of WSI and proper metadata using blockchain and smart contract techniques. Using information retrieval and semantic web techniques, the proposed data framework can allow users to discover datasets and notebooks from distributed sources and promote efficient workflow construction. The data management framework integrates and builds on advanced features in cloud computing and blockchain to address the needs of modern information architectures.
AUTOMATIC WSI INTERPRETATION AND NOVEL PATTERN IDENTIFICATION
CLARIFY has developed novel and robust methods for data-driven WSI interpretation across the three selected diseases. Deep neural network models of the state of the art and tailored architectures were used for feature extraction and classification for different tasks. Preprocessing pipelines were proposed for the automatic detection and removal of artifacts, crucial for weakly supervised learning, inference, and quality assessment of WSIs. CLARIFY introduced both diagnostic and prognostic classification pipelines for the example diseases, as well as content-based image retrieval for WSI with efficient feature-matching strategies. Various learning strategies were adopted depending on the annotations available and the task type, including fully supervised, weakly-supervised, semi-supervised, and unsupervised learning. Different data labeling methods were explored, including active learning and crowdsourcing. The incorporation of explainable AI techniques provided interpretability and facilitated new insights, especially in prognostic tasks.
APPLICATIONS FOR DIGITAL PATHOLOGY
CLARIFY has leveraged artificial intelligence (AI) models to objectively evaluate clinicopathologic parameters and to improve the diagnostic process for the selected diseases, standardizing assessments, reducing variability among pathologists and thereby enhancing tumor classification and histopathological interpretation.
CLARIFY has provided insights into the field of digital pathology to develop tailored technical solutions. As a proof of concept, a prototype of a web application targeting pathologists was established, incorporating several methods developed in the project.
