Abstract:
The use of deep learning networks for medical image classification is becoming increasingly popular. However, annotated datasets for medical diagnosis are still difficult...Show MoreMetadata
Abstract:
The use of deep learning networks for medical image classification is becoming increasingly popular. However, annotated datasets for medical diagnosis are still difficult to obtain due to the Limitations of expertise and expensive consumption. In the absence of datasets, the effectiveness, and robustness of the networks are weak for traditional deep networks for medical diagnosis and problems with these kinds of data issues are quite common. Due to the long-tailed distribution of data and the lack of labeling data for some diseases, traditional deep networks and training techniques can suffer from significant overfitting and poor generalization. We examine the issue of locating the locations of impacted lesions in the absence of data and long-tailed distributions by building on recent developments in contrastive learning. The processing of each image—that is, its division into multiple smaller pictures and comparison between them—improves the ability to identify illnesses. Due to the modest variations between the sick and healthy areas of the disease and the necessity for the network to concentrate on the diseased portions of the disease, contrastive learning allows for the maintenance of intra-class undistorted properties. Our technique can swiftly identify an image’s essential details while masking as much superfluous information a spossible. A s a result of the pre-trained network’s increased sensitivity to image attributes, the network model is better able to generalize to samples and is more resilient when dealing with small sample sizes. Classification models when there are few training samples and a long tail distribution; however, when the training dataset is bigger, our method does not necessarily worse than conventional approaches, because few-shot learning is based on conventional techniques, few-shot learning still trains the classical network with the basis tasks. On the ISIC 2018 and internal ophthalmology datasets, our for the first time tests compari...
Date of Conference: 06-08 December 2022
Date Added to IEEE Xplore: 02 January 2023
ISBN Information: