Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detection

TL;DR

This paper surveys how advanced DL methods—reinforcement learning, federated learning, transfer learning, transformers, and large language models—address cancer detection across imaging, pathology, and omics data. It highlights how RL optimizes diagnostic pathways, FL preserves privacy in multi-institution settings, TL mitigates data scarcity, and Transformers/LLMs enable multimodal and text-rich analysis. Key contributions include mapping techniques to cancer-detection tasks, outlining evaluation metrics and datasets, and identifying challenges such as data imbalance and interpretability with proposed remedies like data augmentation and RAG for LLMs. The work provides a comprehensive resource for researchers and clinicians to adopt and adapt these techniques for improved detection accuracy, generalization, and clinical decision support.

Abstract

The rapid advancement of deep learning (DL) has transformed healthcare, particularly in cancer detection and diagnosis. DL surpasses traditional machine learning and human accuracy, making it a critical tool for identifying diseases. Despite numerous reviews on DL in healthcare, a comprehensive analysis of its role in cancer detection remains limited. Existing studies focus on specific aspects, leaving gaps in understanding its broader impact. This paper addresses these gaps by reviewing advanced DL techniques, including transfer learning (TL), reinforcement learning (RL), federated learning (FL), Transformers, and large language models (LLMs). These approaches enhance accuracy, tackle data scarcity, and enable decentralized learning while maintaining data privacy. TL adapts pre-trained models to new datasets, improving performance with limited labeled data. RL optimizes diagnostic pathways and treatment strategies, while FL fosters collaborative model development without sharing sensitive data. Transformers and LLMs, traditionally used in natural language processing, are now applied to medical data for improved interpretability. Additionally, this review examines these techniques' efficiency in cancer diagnosis, addresses challenges like data imbalance, and proposes solutions. It serves as a resource for researchers and practitioners, providing insights into current trends and guiding future research in advanced DL for cancer detection.

Figures (15)

• Figure 1: The general process of cancer detection via DL
• Figure 3: Road-map outlining the structure of the review.
• Figure 4: Application of advanced DL methods in healthcare, showcasing technologies such as RL, FL, TL, ViT, and LLM. It highlights scenarios like real-time decision-making, privacy-preserving model training, and personalized treatment. The ecosystem integrates agents like hospitals, doctors, and ambulances with cloud computing, pretrained models, and aggregation servers. Key use cases include optimizing resource allocation, enhancing diagnostic accuracy, and improving scalability. Advanced DL solutions address challenges such as data privacy, limited labeled datasets, and computational efficiency, ultimately supporting accurate, efficient, and personalized patient care zhang2023applying.
• Figure 5: SL for ultrasound diagnosis of breast cancer images using both fully-supervised and weakly-supervised DL algorithms han2020semi.
• Figure 6: An example on USL for cancer diagnosis using AE yuan2020unsupervised. A 5-layer AE is trained in an USL manner to extract features from high-dimensional gene expression data, compressing them into a 30-dimensional transcriptomic feature vector. These feature vectors are subsequently used for supervised training in a fully connected deep softmax classifier, which distinguishes between normal and tumor samples and classify tumors by grade and stage.