Cats or CAT scans: transfer learning from natural or medical image source datasets?

Transfer learning is a widely used strategy in medical image analysis. Instead of only training a network with a limited amount of data from the target task of interest, we can first train the network with other, potentially larger source datasets, creating a more robust model. The source datasets do not have to be related to the target task. For a classification task in lung CT images, we could use both head CT images, or images of cats, as the source. While head CT images appear more similar to lung CT images, the number and diversity of cat images might lead to a better model overall. In this survey we review a number of papers that have performed similar comparisons. Although the answer to which strategy is best seems to be "it depends", we discuss a number of research directions we need to take as a community, to gain more understanding of this topic.Comment: Accepted to Current Opinion in Biomedical Engineerin

Automatic Classification of Human Epithelial Type 2 Cell Indirect Immunofluorescence Images using Cell Pyramid Matching

This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The IIF protocol on HEp-2 cells has been the hallmark method to identify the presence of ANAs, due to its high sensitivity and the large range of antigens that can be detected. However, it suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (eg. speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. We propose a novel automatic cell image classification method termed Cell Pyramid Matching (CPM), which is comprised of regional histograms of visual words coupled with the Multiple Kernel Learning framework. We present a study of several variations of generating histograms and show the efficacy of the system on two publicly available datasets: the ICPR HEp-2 cell classification contest dataset and the SNPHEp-2 dataset.Comment: arXiv admin note: substantial text overlap with arXiv:1304.126

APPLICATION OF DEEP CONVOLUTIONAL NETWORK FOR THE CLASSIFICATION OF AUTO IMMUNE DISEASE

Abstract—Indirect Immuno Fluorescence (IIF) detection analysis technique is in limelight because of its great importance in the field of medical health. It is mainly used for the analysis of auto-immune diseases. These diseases are caused when body’s natural defense system can’t distinguish between normal body cells and foreign cells. More than 80 auto-immune diseases exist in humans which affect different parts of body. IIF works both manually as well as by using Computer-Aided Diagnosis (CAD). The aim of research is to propose an advanced methodology for the analysis of auto-immune diseases by using well-known model of transfer learning for the analysis of autoimmune diseases. Data augmentation and data normalization is also used to resolve the problem of over fitting in data. Firstly, freely available MIVIA data set of HEP- type 2 cells has been selected, which contains total of 1457 images and six different classes of staining patterns named as centromere, homogeneous, nucleolar, coarse speckled, fine speckled and cytoplasmatic. Then well-known model of transfer learning VGG-16 are train on MIVIA data set of HEP-type 2 cells. Data augmentation and data normalization used on pre-trained data to avoid over fitting because datasets of medical images are not very large. After the application of data augmentation and data normalization on pre-trained model, the performance of model is used to calculate by using a confusion matrix of VGG-16. VGG-16 achieves 84.375% accuracy. It is more suitable for the analysis of auto-immune diseases. Same as accuracy, we also use the other three parameters, Precision, F1 measures, and recall to check the performance of model. All four parameters use confusion matrix to find performance of model. The tools and languages also have great importance because it gives a simple and easy way of implementation to solve problems in image processing. For this purpose, python and colab is used to read and write the data because python provides fast execution of data and colab work as a simulator of python. The result shows that transfer learning is the most sufficient and enhanced technique for the analysis of auto-immune diseases since it provides high accuracy in less time and reduces the errors in image classification

Pattern Classification of Human Epithelial Images

This project shows an important role to diagnosis autoimmune disorder which is by a comparative analysis on the most appropriate clustering technique for the segmentation and also to develop algorithm for positivity classification. In this project, there are four stages will be used to analyze pattern classification in human epithelial (HEp-2) images. First of all, image enhancement will take part in order to boost efficiency of algorithm by implementing some of the adjustment and filtering technique to increase the visibility of image. After that, the second stage will be the image segmentation by using most appropriate clustering technique. There will be a comparative analysis on clustering techniques for segmentation which are adaptive fuzzy c-mean and adaptive fuzzy moving k-mean. Then, for features extraction, by calculating the mean of each of the properties such as area, perimeter, major axis length, and minor axis length for each images. After that, will implementing a grouping based on properties dataset that has been calculated. Last but not least, from the mean of properties, it will classify into the pattern after ranging the value of mean properties of each of the pattern itself that has been done in classification stage

Deep CNN for IIF Images Classification in Autoimmune Diagnostics

The diagnosis and monitoring of autoimmune diseases are very important problem in medicine. The most used test for this purpose is the antinuclear antibody (ANA) test. An indirect immunofluorescence (IIF) test performed by Human Epithelial type 2 (HEp-2) cells as substrate antigen is the most common methods to determine ANA. In this paper we present an automatic HEp-2 specimen system based on a convolutional neural network method able to classify IIF images. The system consists of a module for features extraction based on a pre-trained AlexNet network and a classification phase for the cell-pattern association using six support vector machines and a k-nearest neighbors classifier. The classification at the image-level was obtained by analyzing the pattern prevalence at cell-level. The layers of the pre-trained network and various system parameters were evaluated in order to optimize the process. This system has been developed and tested on the HEp-2 images indirect immunofluorescence images analysis (I3A) public database. To test the generalisation performance of the method, the leave-one-specimen-out procedure was used in this work. The performance analysis showed an accuracy of 96.4% and a mean class accuracy equal to 93.8%. The results have been evaluated comparing them with some of the most representative works using the same database