Unsupervised Deep Transfer Feature Learning for Medical Image Classification

Abstract

The accuracy and robustness of image classification with supervised deep learning are dependent on the availability of large-scale, annotated training data. However, there is a paucity of annotated data available due to the complexity of manual annotation. To overcome this problem, a popular approach is to use transferable knowledge across different domains by: 1) using a generic feature extractor that has been pre-trained on large-scale general images (i.e., transferlearned) but which not suited to capture characteristics from medical images; or 2) fine-tuning generic knowledge with a relatively smaller number of annotated images. Our aim is to reduce the reliance on annotated training data by using a new hierarchical unsupervised feature extractor with a convolutional auto-encoder placed atop of a pre-trained convolutional neural network. Our approach constrains the rich and generic image features from the pre-trained domain to a sophisticated representation of the local image characteristics from the unannotated medical image domain. Our approach has a higher classification accuracy than transfer-learned approaches and is competitive with state-ofthe-art supervised fine-tuned methods.

Scene Classification for Sports Video Summarization Using Transfer Learning

“…The primary audience of the proposed method is the video summarization and video description researcher community. However, classification, in general, can also be employed in several other domains, i.e., medical image analysis [2,3], agriculture classification [4], biological organism classification, self-driving vehicles [5], video captioning and other similar domains.…”

Retinal Image Classification by Self-supervised Fuzzy Clustering Network

“…There exists several proposed deep learning based unsupervised medical image classification models by transfer learning or clustering based methodologies [3], [4], [31], [36]. From the view of transfer learning methods, Ahn et al [3] utilized transferable knowledge across different domains aiming at reducing the reliance on annotated training data by using a new hierarchical unsupervised feature extractor with a convolutional auto-encoder placed a top of a pre-trained convolutional neural network; Tang et al [36] combined active learning and transfer learning for medical data classification, which is iteratively querying a small number of informative unlabeled target samples, and removing the source samples conflicted with distribution of target data.…”

“…There exists several proposed deep learning based unsupervised medical image classification models by transfer learning or clustering based methodologies [3], [4], [31], [36]. From the view of transfer learning methods, Ahn et al [3] utilized transferable knowledge across different domains aiming at reducing the reliance on annotated training data by using a new hierarchical unsupervised feature extractor with a convolutional auto-encoder placed a top of a pre-trained convolutional neural network; Tang et al [36] combined active learning and transfer learning for medical data classification, which is iteratively querying a small number of informative unlabeled target samples, and removing the source samples conflicted with distribution of target data. As for clustering based methods, Ahn et al [4] proposed an unsupervised feature learning method to tackle the issue of the large volume of unlabeled medical data that learns feature representations to and then differentiate dissimilar medical images using an ensemble of different contolutional neural networks and K -means clustering; Perkonigg et al [31] presented a method to identify predictive texture patterns in medical images under unsupervised framework, simultaneously encode and cluster medical image patches in a low-dimensional latent space.…”

Extraction and Analysis of Blue Steel Roofs Information Based on CNN Using Gaofen-2 Imageries

“…Hinton et al [8] proposed the concept of a deep frame neural network, this network showed improved performance and reduced complexity of image segmentation [9]. The deep learning model has been widely applied in geography, medicine, and physics [10][11][12][13][14][15][16][17][18]. For image recognition applications, the most important network structure in the deep learning algorithm is the CNN (Convolutional Neural Network) structure, which has the advantage of enabling computers to automatically extract feature information [19].…”