Computational Pathology for Brain Disorders

Gabriel Jimenez (1, 2, 3) , Daniel Racoceanu (1, 3, 2)

1 ICM - Institut du Cerveau = Paris Brain Institute
2 ARAMIS - Algorithms, models and methods for images and signals of the human brain = Algorithmes, modèles et méthodes pour les images et les signaux du cerveau humain [ICM Paris]
3 SU - Sorbonne Université

Non-invasive brain imaging techniques allow understanding the behavior and macro changes in the brain to determine the progress of a disease. However, computational pathology provides a deeper understanding of brain disorders at cellular level, able to consolidate a diagnosis and make the bridge between the medical image and the omics analysis. In traditional histopathology, histology slides are visually inspected, under the microscope, by trained pathologists. This process is time-consuming and labor-intensive; therefore, the emergence of Computational Pathology has triggered great hope to ease this tedious task and make it more robust. This chapter focuses on understanding the state-of-the-art machine learning techniques used to analyze whole slide images within the context of brain disorders. We present a selective set of remarkable machine learning algorithms providing discriminative approaches and quality results on brain disorders. These methodologies are applied to different tasks, such as monitoring mechanisms contributing to disease progression and patient survival rates, analyzing morphological phenotypes for classification and quantitative assessment of disease, improving clinical care, diagnosing tumor specimens, and intraoperative interpretation. Thanks to the recent progress in machine learning algorithms for high-content image processing, computational pathology marks the rise of a new generation of medical discoveries and clinical protocols, including in brain disorders.

Mots clés: computational pathology; digital pathology; whole slide imaging; machine learning; deep learning; brain disorders

Domaines: Imagerie médicale; Apprentissage [cs.LG]

Gabriel Jimenez, Daniel Racoceanu. Computational Pathology for Brain Disorders. O. Colliot (Ed.). Machine Learning for Brain Disorders, 197, Springer; Humana, New York, NY, pp.533-572, 2023, Part of the Neuromethods book series (NM,volume 197), 978-1-0716-3195-9. ⟨10.1007/978-1-0716-3195-9_18⟩. ⟨hal-03936550⟩