Deep Learning for Medical Image-Based Cancer Diagnosis

Area of Science:

• Artificial intelligence and computer vision
• Medical imaging analysis
• Deep learning applications

Background:

• Deep learning is a key area in AI for cancer diagnosis using medical images.
• Medical imaging presents unique complexities requiring high accuracy and timeliness in cancer detection.
• A comprehensive review is needed to understand the current state of AI in cancer diagnosis.

Purpose of the Study:

• To review the application of deep learning in medical image-based cancer diagnosis.
• To introduce advanced neural network architectures and overfitting prevention methods.
• To summarize the successes and challenges of deep learning in cancer analysis.

Main Methods:

• Review of five radiological and histopathological image types (X-ray, US, CT, MRI, PET).
• Comprehensive overview of deep learning architectures, pre-trained models, and advanced networks (e.g., ViT, GNN).
• Summary of overfitting prevention techniques (e.g., batch normalization, dropout, data augmentation).

Main Results:

• Deep learning demonstrates success in image classification, reconstruction, detection, segmentation, registration, and synthesis for cancer diagnosis.
• Key challenges include a lack of high-quality labeled datasets, rare cancer diagnosis, multi-modal fusion, model explainability, and generalization.
• Deep learning models have achieved significant results across various medical imaging modalities.

Conclusions:

• Public, standardized cancer databases are essential for advancing deep learning research.
• Further development of pre-trained deep neural networks, multimodal data fusion, and supervised paradigms is crucial.
• Emerging technologies like Vision Transformer (ViT), ensemble learning, and few-shot learning hold significant potential for future cancer diagnosis.