AIM and Brain Tumors

Jakub Nalepa

doi:10.1007/978-3-030-64573-1_284

AIM and Brain Tumors

Jakub Nalepa

Department of Algorithmics and Software (RAU/RAU5)

Future Processing

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

Brain tumors are among the deadliest human cancers, and despite decades of intensive research the survival for many types of malignant primary brain tumors has not improved significantly. Since we continuously generate enormous amounts of clinical data of various modalities that help clinicians not only diagnose brain tumors, but also monitor, quantify, and assess the treatment process, implementing data-driven approaches to analyze such complex data automatically is becoming extremely important. In this chapter, we review artificial intelligence (Al)-powered approaches for this task, and discuss how AI can bring value into the clinical setting through automating tedious data analysis tasks, and extracting information from medical data that may directly affect the treatment pathway.

Original language	English
Title of host publication	Artificial Intelligence in Medicine
Publisher	Springer International Publishing
Pages	1717-1732
Number of pages	16
ISBN (Electronic)	9783030645731
ISBN (Print)	9783030645724
DOIs	https://doi.org/10.1007/978-3-030-64573-1_284
Publication status	Published - 1 Jan 2022

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

ASJC Scopus subject areas

General Medicine
General Biochemistry,Genetics and Molecular Biology

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10.1007/978-3-030-64573-1_284

Cite this

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ER -

AIM and Brain Tumors

Abstract

UN SDGs

ASJC Scopus subject areas

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