Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis

The availability of large-scale annotated image datasets and recent advances in supervised deep learning methods enable the end-to-end derivation of representative image features that can impact a variety of image analysis problems. Such supervised approaches, however, are difficult to implement in the medical domain where large volumes of labelled data are difficult to obtain due to the complexity of manual annotation and inter- and intra-observer variability in label assignment. We propose a new convolutional sparse kernel network (CSKN), which is a hierarchical unsupervised feature learning framework that addresses the challenge of learning representative visual features in medical image analysis domains where there is a lack of annotated training data. Our framework has three contributions: (i) We extend kernel learning to identify and represent invariant features across image sub-patches in an unsupervised manner. (ii) We initialise our kernel learning with a layer-wise pre-training scheme that leverages the sparsity inherent in medical images to extract initial discriminative features. (iii) We adapt a multi-scale spatial pyramid pooling (SPP) framework to capture subtle geometric differences between learned visual features. We evaluated our framework in medical image retrieval and classification on three public datasets. Our results show that our CSKN had better accuracy when compared to other conventional unsupervised methods and comparable accuracy to methods that used state-of-the-art supervised convolutional neural networks (CNNs). Our findings indicate that our unsupervised CSKN provides an opportunity to leverage unannotated big data in medical imaging repositories.Comment: Accepted by Medical Image Analysis (with a new title 'Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis'). The manuscript is available from following link (https://doi.org/10.1016/j.media.2019.06.005

Classification and Retrieval of Digital Pathology Scans: A New Dataset

In this paper, we introduce a new dataset, \textbf{Kimia Path24}, for image classification and retrieval in digital pathology. We use the whole scan images of 24 different tissue textures to generate 1,325 test patches of size 1000$\times$1000 (0.5mm$\times$0.5mm). Training data can be generated according to preferences of algorithm designer and can range from approximately 27,000 to over 50,000 patches if the preset parameters are adopted. We propose a compound patch-and-scan accuracy measurement that makes achieving high accuracies quite challenging. In addition, we set the benchmarking line by applying LBP, dictionary approach and convolutional neural nets (CNNs) and report their results. The highest accuracy was 41.80\% for CNN.Comment: Accepted for presentation at Workshop for Computer Vision for Microscopy Image Analysis (CVMI 2017) @ CVPR 2017, Honolulu, Hawai

Medical images modality classification using multi-scale dictionary learning

In this paper, we proposed a method for classification of medical images captured by different sensors (modalities) based on multi-scale wavelet representation using dictionary learning. Wavelet features extracted from an image provide discrimination useful for classification of medical images, namely, diffusion tensor imaging (DTI), magnetic resonance imaging (MRI), magnetic resonance angiography (MRA) and functional magnetic resonance imaging (FRMI). The ability of On-line dictionary learning (ODL) to achieve sparse representation of an image is exploited to develop dictionaries for each class using multi-scale representation (wavelets) feature. An experimental analysis performed on a set of images from the ICBM medical database demonstrates efficacy of the proposed method

SIFT and color feature fusion using localized maximum-margin learning for scene classification

published_or_final_versionThe 3rd International Conference on Machine Vision (ICMV 2010), Hong Kong, China, 28-30 December 2010. In Proceedings of 3rd ICMV, 2010, p. 56-6

A MEDICAL X-RAY IMAGE CLASSIFICATION AND RETRIEVAL SYSTEM

Medical image retrieval systems have gained high interest in the scientific community due to the advances in medical imaging technologies. The semantic gap is one of the biggest challenges in retrieval from large medical databases. This paper presents a retrieval system that aims at addressing this challenge by learning the main concept of every image in the medical database. The proposed system contains two modules: a classification/annotation and a retrieval module. The first module aims at classifying and subsequently annotating all medical images automatically. SIFT (Scale Invariant Feature Transform) and LBP (Local Binary Patterns) are two descriptors used in this process. Image-based and patch-based features are used as approaches to build a bag of words (BoW) using these descriptors. The impact on the classification performance is also evaluated. The results show that the classification accuracy obtained incorporating image-based integration techniques is higher than the accuracy obtained by other techniques. The retrieval module enables the search based on text, visual and multimodal queries. The text-based query supports retrieval of medical images based on categories, as it is carried out via the category that the images were annotated with, within the classification module. The multimodal query applies a late fusion technique on the retrieval results obtained from text-based and image-based queries. This fusion is used to enhance the retrieval performance by incorporating the advantages of both text-based and content-based image retrieval