A Whole Slide Image (WSI) is a high-resolution digital image created by scanning an entire glass slide containing a biological specimen, such as tissue sections or cell samples, at multiple magnifications. These images are digitally viewable, analyzable, and shareable, and are widely used for Artificial Intelligence (AI) algorithm development. WSIs play an important role in pathology for disease diagnosis and oncology for cancer research, but are also applied in neurology, veterinary medicine, hematology, microbiology, dermatology, pharmacology, toxicology, immunology, and forensic science. When assembling cohorts for AI training or validation, it is essential to know the content of a WSI. However, no standard currently exists for this metadata, and such a selection has largely relied on manual inspection, which is not suitable for large collections with millions of objects. We propose a general framework to generate 2D index maps (tissue maps) that describe the morphological content of WSIs using common syntax and semantics to achieve interoperability between catalogs. The tissue maps are structured in three layers: source, tissue type, and pathological alterations. Each layer assigns WSI segments to specific classes, providing AI-ready metadata. We demonstrate the advantages of this standard by applying AI-based metadata extraction from WSIs to generate tissue maps and integrating them into a WSI archive. This integration enhances search capabilities within WSI archives, thereby facilitating the accelerated assembly of high-quality, balanced, and more targeted datasets for AI training, validation, and cancer research.

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Automated image analysis and artificial intelligence (AI) are a growing market in digital pathology. While various proprietary pathology systems exist, there are no fully vendor-agnostic integration approaches for AI apps. This makes it difficult for vendors of AI solutions to integrate their products into the multitude of non-standard software systems in pathology. The EMPAIA Consortium (EcosysteM for Pathology Diagnostics with AI Assistance) develops an open and decentralized platform allowing AI-based apps of different vendors to be integrated with existing lab IT infrastructures. This is intended to lower the barriers to entry for AI vendors and provide pathologists with access to advanced AI tools. The EMPAIA platform is based on web technologies that can be deployed both on-premises and in the cloud. There are open-source reference implementations for core platform services that can be integrated with or replaced by proprietary alternatives as long as they conform to open API specifications. Apps can be obtained through a central marketplace so pathologists can use them in their daily workflow. In this paper, we provide an overview of the EMPAIA platform architecture. We identify critical use cases and requirements for AI-based software platforms in pathology and explain how these are fulfilled by the EMPAIA platform. Finally, we evaluate the efficiency of routing image data through the platform.

The increasing prevalence of digitised workflows in diagnostic pathology opens the door to life-saving applications of artificial intelligence (AI). Explainability is identified as a critical component for the safety, approval and acceptance of AI systems for clinical use. Despite the cross-disciplinary challenge of building explainable AI (xAI), very few application- and user-centric studies in this domain have been carried out. We conducted the first mixed-methods study of user interaction with samples of state-of-the-art AI explainability techniques for digital pathology. This study reveals challenging dilemmas faced by developers of xAI solutions for medicine and proposes empirically-backed principles for their safer and more effective design.

In this paper, cell nuclei attributed relational graphs are extensively studied and comparatively analyzed for effective knowledge description and classification in H&E stained whole slide images of gastric cancer. This includes design and implementation of multiple graph variations with diverse tissue component characteristics and architectural properties to obtain enhanced image representations, followed by hierarchical ensemble learning and classification. A detailed comparative analysis of the proposed graph-based methods, also with the established low-level, object-level and high-level image descriptions is performed, that further leads to a hybrid approach combining salient visual information. Quantitative evaluation of investigated methods suggests the suitability of particular graph variants for automatic classification using H&E stained histopathological gastric cancer whole slide images based on HER2 immunohistochemistry.

Automatic detection of necrosis in histological images is an interesting problem of digital pathology that needs to be addressed. Determination of presence and extent of necrosis can provide useful information for disease diagnosis and prognosis, and the detected necrotic regions can also be excluded before analyzing the remaining living tissue. This paper describes a novel appearance-based method to detect tumor necrosis in histopathogical whole slide images. Studies are performed on heterogeneous microscopic images of gastric cancer containing tissue regions with variation in malignancy level and stain intensity. Textural image features are extracted from image patches to efficiently represent necrotic appearance in the tissue and machine learning is performed using support vector machines followed by discriminative thresholding for our complex datasets. The classification results are quantitatively evaluated for different image patch sizes using two cross validation approaches namely three-fold and leave one out cross validation, and the best average cross validation rate of 85.31% is achieved for the most suitable patch size. Therefore, the proposed method is a promising tool to detect necrosis in heterogeneous whole slide images, showing its robustness to varying visual appearances.