Pattern recognition

The availability of medical imaging data from clinical archives, research literature, and clinical manuals, coupled with recent advances in computer vision offer the opportunity for image-based diagnosis, teaching, and biomedical research. However, the content and semantics of an image can vary depending on its modality and as such the identification of image modality is an important preliminary step. The key challenge for automatically classifying the modality of a medical image is due to the visual characteristics of different modalities: some are visually distinct while others may have only subtle differences. This challenge is compounded by variations in the appearance of images based on the diseases depicted and a lack of sufficient training data for some modalities. In this paper, we introduce a new method for classifying medical images that uses an ensemble of different convolutional neural network (CNN) architectures. CNNs are a state-of-the-art image classification technique that learns the optimal image features for a given classification task. We hypothesise that different CNN architectures learn different levels of semantic image representation and thus an ensemble of CNNs will enable higher quality features to be extracted. Our method develops a new feature extractor by fine-tuning CNNs that have been initialized on a large dataset of natural images. The fine-tuning process leverages the generic image features from natural images that are fundamental for all images and optimizes them for the variety of medical imaging modalities. These features are used to train numerous multiclass classifiers whose posterior probabilities are fused to predict the modalities of unseen images. Our experiments on the ImageCLEF 2016 medical image public dataset (30 modalities; 6776 training images, and 4166 test images) show that our ensemble of fine-tuned CNNs achieves a higher accuracy than established CNNs. Our ensemble also achieves a higher accuracy than methods in the literature evaluated on the same benchmark dataset and is only overtaken by those methods that source additional training data.

/pdf/an-ensemble-of-fine-tuned-convolutional-neural-networks-for-4oc7swcxuk.pdf

An Ensemble of Fine-Tuned Convolutional Neural Networks for Medical Image Classification.

Human activity recognition (HAR) is one of the most important and challenging problems in the computer vision. It has critical application in wide variety of tasks including gaming, human–robot interaction, rehabilitation, sports, health monitoring, video surveillance, and robotics. HAR is challenging due to the complex posture made by the human and multiple people interaction. Various artifacts that commonly appears in the scene such as illuminations variations, clutter, occlusions, background diversity further adds the complexity to HAR. Sensors for multiple modalities could be used to overcome some of these inherent challenges. Such sensors could include an RGB-D camera, infrared sensors, thermal cameras, inertial sensors, etc. This article introduces a comprehensive review of different multimodal human activity recognition methods where different types of sensors being used along with their analytical approaches and fusion methods. Further, this article presents classification and discussion of existing work within seven rational aspects: (a) what are the applications of HAR; (b) what are the single and multi-modality sensing for HAR; (c) what are different vision based approaches for HAR; (d) what and how wearable sensors based system contributes to the HAR; (e) what are different multimodal HAR methods; (f) how a combination of vision and wearable inertial sensors based system contributes to the HAR; and (g) challenges and future directions in HAR. With a more and comprehensive understanding of multimodal human activity recognition, more research in this direction can be motivated and refined.

A Review of Multimodal Human Activity Recognition with Special Emphasis on Classification, Applications, Challenges and Future Directions

Emerging information technologies for enhanced healthcare

Segmentation and Feature Extraction in Medical Imaging: A Systematic Review

We present AiTLAS: Benchmark Arena -- an open-source benchmark suite for evaluating state-of-the-art deep learning approaches for image classification in Earth Observation (EO). To this end, we present a comprehensive comparative analysis of more than 500 models derived from ten different state-of-the-art architectures and compare them to a variety of multi-class and multi-label classification tasks from 22 datasets with different sizes and properties. In addition to models trained entirely on these datasets, we benchmark models trained in the context of transfer learning, leveraging pre-trained model variants, as it is typically performed in practice. All presented approaches are general and can be easily extended to many other remote sensing image classification tasks not considered in this study. To ensure reproducibility and facilitate better usability and further developments, all of the experimental resources including the trained models, model configurations, and processing details of the datasets (with their corresponding splits used for training and evaluating the models) are publicly available on the repository: https://github.com/biasvariancelabs/aitlas-arena 

Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image Classification

Improved medical image modality classification using a combination of visual and textual features

In this paper we depict an implemented system for medical image retrieval. Our system performs retrieval based on both textual and visual content, separately and combined, using advanced encoding and quantization techniques. The text-based retrieval subsystem uses textual data acquired from an image's corresponding article to generate a suitable representation. Using a vector space model, the generated representations structure is altered to increase performance. Query expansion with pseudo-relevance feedback is applied to fine-tune the results. The content-based retrieval subsystem performs retrieval based on visual features extracted from the images. A Gaussian Mixture Model is constructed from the extracted visual features, in our case - RGB histograms, and is used in encoding the same features into Fisher Vectors. With scalability and speed in mind, we utilized a product quantization technique over the generated vectors, which provides fast response times over large image collections. Product quantization drastically reduces the size of the image representation at almost no cost to accuracy, thus improving the scalability factor of our system. Our system uses modality classification to further improve retrieval results. This subsystem labels the image modality based on their visual content. The images are described using state-of-the-art opponentSIFT visual features. Classification was performed using Support Vector Machines (SVMs). The predictions from the SVMs are used for re-ranking the resulting images based on their modality and the modality of the query. The system was evaluated against the standardized ImageCLEF 2013, 2012 and 2011 medical datasets and it reported state-of-the-art performance for all datasets.

Multimodal medical image retrieval system

This paper presents the details of the participation of FCSE (Faculty of Computer Science and Engineering) research team in Image- CLEF 2013 medical tasks (modality classication, ad-hoc image retrieval and case-based retrieval). For the modality classication task we used SIFT descriptors and tf idf weights of the surrounding text (image caption and paper title) as features. SVMs with 2 kernel and one-vs- all strategy were used as classiers. For the ad-hoc image retrieval task and case-based retrieval we adopted a strategy which uses a combination of word-space and concept-space approaches. The word-space approach uses the Terrier IR search engine to index and retrieve the text associ- ated with the images/cases. The concept-space approach uses Metamap to map the text data into a set of UMLS (Unied Medical Language System) concepts, which are later indexed and retrieved by the Terrier IR search engine. The results from the word-space and concept-space retrieval are fused using linear combination. For the compound gure separation task, we used unsupervised algorithm based on breadth-rst search strategy using only visual information from the medical images. The selected algorithms were tuned and tested on the data from Im- ageCLEF 2012 medical task and based on the selected parameters we submitted the new experiments for ImageCLEF 2013 medical task. We achieved very good overall performance: the best run for the modality classication ranked 2nd in the overall score, the best run for the ad-hoc

/pdf/fcse-at-medical-tasks-of-imageclef-2013-268xhao0gw.pdf

FCSE at Medical Tasks of ImageCLEF 2013

Mammography image classification is a very important research field due to its domain of implementation. The aim of this paper is to compare feature extraction methods and to test them on a variety of classifiers. Five feature extraction methods were used: LBP, GLDM, GLRLM, Haralick and Gabor texture features. Three classification algorithms were used during the experiments, namely, support vector machines, k-nearest neighbor and c4.5 algorithm. The experiments were conducted on the MIAS database. The results show that GLDM is the most appropriate feature extraction method for images from this database.

Comparison of feature extraction algorithms for mammography images

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