Deep Learning Applications in Lymphoma Imaging

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This review highlights advancements in deep learning applications for lymphoma imaging, implying improved diagnostic accuracy and clinical outcomes.

Deep learning models, especially CNNs, are revolutionizing lymphoma imaging by enabling automated detection and classification.
Recent advancements include improving imaging accuracy and integrating AI tools into clinical practice for better patient outcomes.
Challenges persist in obtaining high-quality datasets and addressing biases to ensure model reliability and performance.
Efforts are underway to enhance model interpretability and ensure diverse patient populations to boost generalizability of AI tools.

Sorin et al. (2025) studied this question.

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View Full Paper

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Authors

This review highlights advancements in deep learning applications for lymphoma imaging, implying improved diagnostic accuracy and clinical outcomes.

Deep learning models, especially CNNs, are revolutionizing lymphoma imaging by enabling automated detection and classification.
Recent advancements include improving imaging accuracy and integrating AI tools into clinical practice for better patient outcomes.
Challenges persist in obtaining high-quality datasets and addressing biases to ensure model reliability and performance.
Efforts are underway to enhance model interpretability and ensure diverse patient populations to boost generalizability of AI tools.

Sorin et al. (2025) studied this question.

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