Convolutional sparse kernel network for unsupervised medical image analysis
نویسندگان
چکیده
منابع مشابه
A Radon-based Convolutional Neural Network for Medical Image Retrieval
Image classification and retrieval systems have gained more attention because of easier access to high-tech medical imaging. However, the lack of availability of large-scaled balanced labelled data in medicine is still a challenge. Simplicity, practicality, efficiency, and effectiveness are the main targets in medical domain. To achieve these goals, Radon transformation, which is a well-known t...
متن کاملLearning Document Image Features With SqueezeNet Convolutional Neural Network
The classification of various document images is considered an important step towards building a modern digital library or office automation system. Convolutional Neural Network (CNN) classifiers trained with backpropagation are considered to be the current state of the art model for this task. However, there are two major drawbacks for these classifiers: the huge computational power demand for...
متن کاملSparse Kernel Learning for Image Set Classification
No single universal image set representation can efficiently encode all types of image set variations. In the absence of expensive validation data, automatically ranking representations with respect to performance is a challenging task. We propose a sparse kernel learning algorithm for automatic selection and integration of the most discriminative subset of kernels derived from different image ...
متن کاملMedical image retrieval using deep convolutional neural network
With a widespread use of digital imaging data in hospitals, the size of medical image repositories is increasing rapidly. This causes difficulty in managing and querying these large databases leading to the need of content based medical image retrieval (CBMIR) systems. A major challenge in CBMIR systems is the semantic gap that exists between the low level visual information captured by imaging...
متن کاملA mixed-scale dense convolutional neural network for image analysis.
Deep convolutional neural networks have been successfully applied to many image-processing problems in recent works. Popular network architectures often add additional operations and connections to the standard architecture to enable training deeper networks. To achieve accurate results in practice, a large number of trainable parameters are often required. Here, we introduce a network architec...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Medical Image Analysis
سال: 2019
ISSN: 1361-8415
DOI: 10.1016/j.media.2019.06.005