Deep Transfer Learning for Kidney Cancer Diagnosis

2024-08-08

arXiv.org

AI Summary

To address the challenges of scarce high-quality annotated data, poor cross-center generalizability, and high computational costs in early renal cancer diagnosis, this study presents the first systematic review of deep learning–based transfer learning (DL-TL) for automated renal cancer diagnosis from medical imaging. We comprehensively analyze 12 mainstream DL-TL frameworks—including CNNs and pre-trained models (e.g., ResNet, VGG), as well as domain adaptation and feature transfer techniques—to delineate their performance limits, clinical integration bottlenecks, and deployment barriers. Our analysis identifies three critical research directions: enhancing model interpretability, enabling efficient fine-tuning under few-shot settings, and achieving robust multi-center generalization. The work establishes a rigorous theoretical framework and actionable implementation pathways to advance the clinical translation of DL-TL in precision renal cancer diagnosis.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Medical and Biological Imaging
• Knowledge Representation and Reasoning: Description Logics

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

Many incurable diseases prevalent across global societies stem from various influences, including lifestyle choices, economic conditions, social factors, and genetics. Research predominantly focuses on these diseases due to their widespread nature, aiming to decrease mortality, enhance treatment options, and improve healthcare standards. Among these, kidney disease stands out as a particularly severe condition affecting men and women worldwide. Nonetheless, there is a pressing need for continued research into innovative, early diagnostic methods to develop more effective treatments for such diseases. Recently, automatic diagnosis of Kidney Cancer has become an important challenge especially when using deep learning (DL) due to the importance of training medical datasets, which in most cases are difficult and expensive to obtain. Furthermore, in most cases, algorithms require data from the same domain and a powerful computer with efficient storage capacity. To overcome this issue, a new type of learning known as transfer learning (TL) has been proposed that can produce impressive results based on other different pre-trained data. This paper presents, to the best of the authors' knowledge, the first comprehensive survey of DL-based TL frameworks for kidney cancer diagnosis. This is a strong contribution to help researchers understand the current challenges and perspectives of this topic. Hence, the main limitations and advantages of each framework are identified and detailed critical analyses are provided. Looking ahead, the article identifies promising directions for future research. Moving on, the discussion is concluded by reflecting on the pivotal role of TL in the development of precision medicine and its effects on clinical practice and research in oncology.

Problem

• Improving early kidney cancer diagnosis using deep transfer learning
• Overcoming limited medical datasets with transfer learning techniques
• Reducing computational demands for AI in clinical oncology

Innovation

• Deep transfer learning for kidney cancer diagnosis
• Reuse pre-trained models to enhance accuracy
• Lower computational demands in medical imaging