Transfer Learning Strategies for Fine-Tuning Pretrained Convolutional Neural Networks in Medical Imaging

Abstract

In the area of medical imaging, transfer learning has become a potent technique that uses pretrained Convolutional Neural Networks (CNNs) to improve the performance of particular tasks. An overview of several transfer learning techniques used for optimising pretrained CNNs in the context of medical image analysis is given in this abstract. The size limitations of medical imaging datasets make it difficult to train deep learning models from scratch. Pre-trained CNNs are a good place to start, such as those that have been trained on huge natural picture datasets like ImageNet. When these pre-trained models are applied to medical imaging applications, fine-tuning is frequently used. One common method is feature extraction, where the bottom layers of the pretrained CNN are frozen and operate as feature extractors. Then, for the specific medical task at hand, these features are loaded into a bespoke classifier. The ability of the pretrained network to recognise subtle picture patterns is advantageous in this method. Another strategy is to optimise the CNN architecture as a whole, which enables the model to adjust to the features of medical images. Small learning rates are frequently used in transfer learning techniques to avoid overfitting during fine-tuning. Additionally, to further enhance model generalisation, domain-specific data augmentation is essential. The use of ensemble approaches, which combine several pretrained CNNs, is also investigated. These models are capable of offering various feature representations and improving classification precision. In order to bridge the domain gap between natural photos and medical images, domain adaption techniques are also used. One approach to align feature distributions is by adversarial training, while another is through domain-specific batch normalisation. The feature extraction, network fine-tuning, ensemble approaches, and domain adaptation are all part of transfer learning methodologies for optimising pretrained CNNs in medical imaging. Researchers have made great progress using these techniques in a number of medical image processing tasks, proving the value of transfer learning in this important area.