Abstract
The latest advances in artificial intelligence (AI) have significantly transformed cancer imaging, promising substantial improvements in the diagnostic and therapeutic pathways for patients with cancer. By leveraging advanced AI technologies, such as deep learning, researchers have achieved breakthroughs that outperform traditional methods in key scientific and clinical tasks across the wide-ranging fields of cancer visualization and cancer quantification. This chapter delves into the profound integration of AI in cancer imaging, highlighting the technical characteristics and typical applications in the domain. We demonstrate AI's superiority in the non-invasive examination of tumors and their microenvironments, as well as its enhanced capabilities in detection, segmentation, diagnosis, and prognosis, facilitating the personalized treatment plans. Additionally, this chapter explores innovative insights and frameworks for optimizing analysis patterns of imaging data and integrating multimodal data. These innovations are poised to drive future breakthroughs in cancer imaging. Furthermore, despite these advancements, recognizing the increasing clinical demands for privacy protection and predictive accuracy, we discuss the potential bottlenecks and technological opportunities, analyzing the development directions that can facilitate the practical implementation of AI technologies.
| Original language | English |
|---|---|
| Title of host publication | Cancer Theranostics, Second Edition |
| Publisher | Elsevier |
| Pages | 203-222 |
| Number of pages | 20 |
| ISBN (Electronic) | 9780443222535 |
| ISBN (Print) | 9780443222542 |
| DOIs | |
| State | Published - 1 Jan 2025 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Artificial intelligence
- Image reconstruction
- Medical image analysis
- Radiomics
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