| Abstract (ENG): |
Rapid advances in computer vision (CV) and artificial intelligence have opened new avenues for digital pathology, including the diagnosis and treatment of central nervous system (CNS) tumors. In addition to reviewing the state-of-the-art in CV-based digital pathology and highlighting its potential to revolutionize the field, this chapter also provides a general introduction to digital pathology and Machine Learning (ML) for neuropathologists. Although currently limited to research, the integration of CV tools into digital pathology already offers significant advantages, such as automating tissue analysis and providing quantitative assessments. The transition from research to clinical application is slowly gaining momentum. To provide neuropathologists with the necessary skills to succeed in digital pathology and ML, the chapter also discusses how physicians and researchers can create custom models and tools tailored to specific needs using tools such as nnU-Net, deepflash2, and PathML. Emphasis is placed on the importance of interdisciplinary collaboration and continued research to fully realize the potential of CV in digital pathology for CNS tumors, to address the challenges of workforce shortages and increased workloads in neuropathology. |
| Citation: |
Hieber, Daniel and Holl, Felix and Nickl, Vera and Liesche-Starnecker, Friederike and Schobel, Johannes
(2024)
Perspective Chapter: Computer Vision-Based Digital Pathology for Central Nervous System Tumors – State-of-the-Art and Current Advances.
In:
Advanced Concepts and Strategies in Central Nervous System Tumors / Chow, Frances ; Hwang, Lindsay (Eds).
London: IntechOpen.
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