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1
Esophageal cancer is a prevalent and aggressive malignancy, necessitating early detection and precise diagnosis for effective treatment.
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2
Artificial intelligence models enhance pathology workflows by improving early screening, diagnostic accuracy, and prognostic predictions in esophageal cancer.
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3
AI architectures like CNNs and U-Net are foundational for image analysis tasks, aiding in tumor identification and histopathological classification.
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4
Multimodal fusion strategies in AI pathology integrate diverse data types, enhancing model performance and capturing complex associations.
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5
Challenges remain in AI performance and societal implications, highlighting the need for ongoing research in precision oncology and pathology efficiency.
