A Deep Multi-Label Segmentation Network For Eosinophilic Esophagitis Whole Slide Biopsy Diagnostics

CADUCEO: A Platform to Support Federated Healthcare Facilities through Artificial Intelligence

Data-driven algorithms have proven to be effective for a variety of medical tasks, including disease categorization and prediction, personalized medicine design, and imaging diagnostics. Although their performance is frequently on par with that of clinicians, their widespread use is constrained by a number of obstacles, including the requirement for high-quality data that are typical of the population, the difficulty of explaining how they operate, and ethical and regulatory concerns. The use of data augmentation and synthetic data generation methodologies, such as federated learning and explainable artificial intelligence ones, could provide a viable solution to the current issues, facilitating the widespread application of artificial intelligence algorithms in the clinical application domain and reducing the time needed for prevention, diagnosis, and prognosis by up to 70%. To this end, a novel AI-based functional framework is conceived and presented in this paper.

DEPAS: De-novo Pathology Semantic Masks using a Generative Model

The integration of artificial intelligence (AI) into digital pathology has the potential to automate and improve various tasks, such as image analysis and diagnostic decision-making. Yet, the inherent variability of tissues, together with the need for image labeling, lead to biased datasets that limit the generalizability of algorithms trained on them. One of the emerging solutions for this challenge is synthetic histological images. Debiasing real datasets require not only generating photorealistic images but also the ability to control the cellular features within them. A common approach is to use generative methods that perform image translation between semantic masks that reflect prior knowledge of the tissue and a histological image. However, unlike other image domains, the complex structure of the tissue prevents a simple creation of histology semantic masks that are required as input to the image translation model, while semantic masks extracted from real images reduce the process's scalability. In this work, we introduce a scalable generative model, coined as DEPAS (De-novo Pathology Semantic Masks), that captures tissue structure and generates high-resolution semantic masks with state-of-the-art quality. We demonstrate the ability of DEPAS to generate realistic semantic maps of tissue for three types of organs: skin, prostate, and lung. Moreover, we show that these masks can be processed using a generative image translation model to produce photorealistic histology images of two types of cancer with two different types of staining techniques. Finally, we harness DEPAS to generate multi-label semantic masks that capture different cell types distributions and use them to produce histological images with on-demand cellular features. Overall, our work provides a state-of-the-art solution for the challenging task of generating synthetic histological images while controlling their semantic information in a scalable way.

Between Generating Noise and Generating Images: Noise in the Correct Frequency Improves the Quality of Synthetic Histopathology Images for Digital Pathology

Artificial intelligence and machine learning techniques have the promise to revolutionize the field of digital pathology. However, these models demand considerable amounts of data, while the availability of unbiased training data is limited. Synthetic images can augment existing datasets, to improve and validate AI algorithms. Yet, controlling the exact distribution of cellular features within them is still challenging. One of the solutions is harnessing conditional generative adversarial networks that take a semantic mask as an input rather than a random noise. Unlike other domains, outlining the exact cellular structure of tissues is hard, and most of the input masks depict regions of cell types. This is also the case for non-small cell lung cancer, the most common type of lung cancer. Deciding whether a patient would receive immunotherapy depends on quantifying regions of stained cells. However, using polygon-based masks introduce inherent artifacts within the synthetic images - due to the mismatch between the polygon size and the single-cell size. In this work, we show that introducing random single-pixel noise with the appropriate spatial frequency into a polygon semantic mask can dramatically improve the quality of the synthetic images. We used our platform to generate synthetic images of immunohistochemistry-treated lung biopsies. We test the quality of the images using a three-fold validation procedure. First, we show that adding the appropriate noise frequency yields 87% of the similarity metrics improvement that is obtained by adding the actual single-cell features. Second, we show that the synthetic images pass the Turing test. Finally, we show that adding these synthetic images to the train set improves AI performance in terms of PD-L1 semantic segmentation performances. Our work suggests a simple and powerful approach for generating synthetic data on demand to unbias limited datasets to improve the algorithms' accuracy and validate their robustness.

Harnessing artificial intelligence to infer novel spatial biomarkers for the diagnosis of eosinophilic esophagitis

Eosinophilic esophagitis (EoE) is a chronic allergic inflammatory condition of the esophagus associated with elevated esophageal eosinophils. Second only to gastroesophageal reflux disease, EoE is one of the leading causes of chronic refractory dysphagia in adults and children. EoE is a clinicopathologic disorder and the histological portion of the diagnosis requires enumerating the density of esophageal eosinophils in esophageal biopsies, and evaluating additional features such as basal zone hyperplasia is helpful. However, this task requires time-consuming, somewhat subjective manual analysis, thus reducing the ability to process the complex tissue structure and infer its relationship with the patient's clinical status. Previous artificial intelligence (AI) approaches that aimed to improve histology-based diagnosis focused on recapitulating identification and quantification of the area of maximal eosinophil density, the gold standard manual metric for determining EoE disease activity. However, this metric does not account for the distribution of eosinophils or other histological features, over the whole slide image. Here, we developed an artificial intelligence platform that infers local and spatial biomarkers based on semantic segmentation of intact eosinophils and basal zone distributions. Besides the maximal density of eosinophils [referred to as Peak Eosinophil Count (PEC)] and a maximal basal zone fraction, we identify the value of two additional metrics that reflect the distribution of eosinophils and basal zone fractions. This approach enables a decision support system that predicts EoE activity and potentially classifies the histological severity of EoE patients. We utilized a cohort that includes 1,066 biopsy slides from 400 subjects to validate the system's performance and achieved a histological severity classification accuracy of 86.70%, sensitivity of 84.50%, and specificity of 90.09%. Our approach highlights the importance of systematically analyzing the distribution of biopsy features over the entire slide and paves the way toward a personalized decision support system that will assist not only in counting cells but can also potentially improve diagnosis and provide treatment prediction.