Vol. 8, Special Issue 1 (2019)

The integration of deep transfer learning with medical image analysis holds great promise for improving diagnostic accuracy and efficiency. This survey begins by elucidating the fundamental principles of deep transfer learning, highlighting its capacity to transfer knowledge learned from one domain to another, thereby mitigating the challenge of limited labeled medical data. It explores the evolution of transfer learning architectures, from traditional models to state-of-the-art deep neural networks, and assesses their applicability to diverse medical imaging datasets.

The review provides an in-depth analysis of the key challenges encountered in multi-modal disease detection and how deep transfer learning techniques address these challenges. It delves into the intricacies of feature extraction and representation learning, shedding light on how these processes enhance the model's ability to discern subtle patterns indicative of various diseases. Furthermore, the paper discusses the integration of clinical data, genetic information, and other non-imaging modalities, emphasizing the potential of deep transfer learning to exploit synergies among multiple data sources.

A critical aspect of this survey is the exploration of real-world applications and case studies where deep transfer learning has demonstrated significant advancements in disease detection. The comprehensive evaluation includes a discussion on the performance metrics used to assess the efficacy of these models, considering factors such as sensitivity, specificity, and interpretability.

The review concludes with insights into future directions and challenges in the rapidly evolving landscape of deep transfer learning for multi-modal disease detection. It underscores the need for standardized benchmarks, ethical considerations, and continued collaboration between the medical and artificial intelligence communities to ensure the responsible and effective deployment of these innovative technologies in clinical practice. Overall, this survey serves as a valuable resource for researchers, clinicians, and practitioners seeking a comprehensive understanding of the current state and future prospects of deep transfer learning in multi-modal disease detection.