Deep Learning and Pathology: Innovative Applications in Cancer Diagnosis and Prognosis
A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".
Deadline for manuscript submissions: 30 November 2024 | Viewed by 308
Special Issue Editor
Special Issue Information
Dear Colleagues,
The convergence of deep learning and pathology represents a transformative frontier in medical science, offering unprecedented capabilities in the diagnosis and prognosis of cancer.
Recent advancements in biotechnology have resulted in an exponential increase in biological data, from molecular levels (such as gene functions and protein interactions) to clinical settings (including imaging and electronic medical records). Traditional data analysis methods are often insufficient to handle this complex and heterogeneous data. However, deep learning, with its powerful algorithms like convolutional neural networks and recurrent neural networks, has emerged as a crucial tool for extracting meaningful insights from these vast datasets.
This Special Issue seeks to showcase the latest developments and applications of deep learning in the field of cancer pathology. The articles within explore innovative techniques and applications that enhance our ability to diagnose cancer more accurately and predict patient outcomes with greater precision.
Topics covered in this issue include, but are not limited to, the following:
- Deep learning in histopathological image analysis: understanding the molecular changes in tissue samples through computer vision.
- Predictive models for cancer prognosis: integrating molecular markers and clinical data to predict cancer progression and patient survival.
- Integration of multi-omics data: combining genomics, transcriptomics, proteomics, and other omics data to gain a comprehensive understanding of cancer at the molecular level.
- Automated detection and classification of tumors: using deep learning to identify and classify tumors based on their molecular signatures.
- Personalized medicine approaches: leveraging molecular data to tailor cancer treatments to the individual patient's needs.
We invite researchers and practitioners to explore these groundbreaking studies and consider the profound impact of deep learning on cancer pathology. The contributions in this issue not only highlight the current state of the field but also pave the way for future innovations that can revolutionize cancer diagnosis and prognosis. Data on molecular mechanisms or pathophysiology are essential, and papers that only contain clinical trials/data are not acceptable.
Dr. Nicolè Lorenzo
Guest Editor
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Keywords
- deep learning
- pathology
- digital pathology
- tissue segmentation
- cancer diagnosis
- prognosis prediction
- multi-dimensional analysis
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