Data Mining Practical Machine Learning Tools and Techniques

Statistics for Spatial Data.

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

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Statistical Parametric Mapping The Analysis Of Functional Brain Images

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2017.0387

Opportunities and obstacles for deep learning in biology and medicine.

This study has developed a novel saliency detection method based on compactness feature for detecting three common types of leakage in retinal fluorescein angiogram: large focal, punctate focal, and vessel segment leakage. Leakage from retinal vessels occurs in a wide range of retinal diseases, such as diabetic maculopathy and paediatric malarial retinopathy. The proposed framework consists of three major steps: saliency detection, saliency refinement and leakage detection. First, the Retinex theory is adapted to address the illumination inhomogeneity problem. Then two saliency cues, intensity and compactness, are proposed for the estimation of the saliency map of each individual superpixel at each level. The saliency maps at different levels over the same cues are fused using an averaging operator. Finally, the leaking sites can be detected by masking the vessel and optic disc regions. The effectiveness of this framework has been evaluated by applying it to different types of leakage images with cerebral malaria. The sensitivity in detecting large focal, punctate focal and vessel segment leakage is 98.1, 88.2 and 82.7 %, respectively, when compared to a reference standard of manual annotations by expert human observers. The developed framework will become a new powerful tool for studying retinal conditions involving retinal leakage.

/pdf/a-compactness-based-saliency-approach-for-leakages-detection-3vfclfha09.pdf

A compactness based saliency approach for leakages detection in fluorescein angiogram

The quantitative analysis of retinal blood vessels is important for the management of vascular disease and tackling problems such as locating blood clots. Such tasks are hampered by the inability to accurately trace back problems along vessels to the source. This is due to the unresolved challenge of distinguishing automatically between vessel branchings and vessel crossings. In this paper, we present a new technique for tackling this challenging problem by developing a convolutional neural network approach for first locating vessel junctions and then classifying them as either branchings or crossings. We achieve a high accuracy of 94% for junction detection and 88% for classification. Combined with work in segmentation, this method has the potential to facilitate automated localisation of blood clots and other disease symptoms leading to improved management of eye disease through aiding or replacing a clinicians diagnosis.

Automatic Detection and Identification of Retinal Vessel Junctions in Colour Fundus Photography

© 2019 for this paper by its authors. Convolutional Neural Networks (CNNs) have been demonstrated to achieve state-of-the-art results on complex computer vision tasks, including medical image diagnosis of Diabetic Retinopathy (DR). CNNs are powerful because they determine relevant image features automatically. However, the current inability to demonstrate what these features are has led to CNNs being considered to be 'black box' methods whose results should not be trusted. This paper presents a method for identifying the learned features of a CNN and applies it in the context of the diagnosis of DR in fundus images using the well-known DenseNet. We train the CNN to diagnose and determine the severity of DR and then successfully extract feature maps from the CNN which identify the regions and features of the images which have led most strongly to the CNN prediction. This feature extraction process has great potential, particularly for encouraging confidence in CNN approaches from users and clinicians, and can aid in the further development of CNN methods. There is also potential for determining previously unidentified features which may contribute to a classification.

Feature visualisation of classification of diabetic retinopathy using a convolutional neural network

In this paper, a multiscale line filter is proposed which is integrated with phase congruency to detect the network of vessels in retinal images. The proposed line filter can reduces the influence of step edges compared with Gaussian matched filter, and the post process use the phase congruency demonstrated a substantial improvement in detecting vessels which have low contrast or minor width. The performance of the proposed method is evaluated on the publicly available databases DRIVE and STARE. The experimental results show that an effective and robust detection can be achieved.

/pdf/retinal-blood-vessel-detection-using-multiscale-line-filter-3cjlj9ut4s.pdf

Retinal blood vessel detection using multiscale line filter and phase congruency

The estimation of vascular network topology in complex networks is important in understanding the relation between vascular changes and a wide spectrum of diseases. Automatic method of analysis of retinal vascular networks is of great assistance to the ophthalmologist in terms of diagnosis and treatment of eye disease. In this paper, we propose a method for estimating retinal vessel topology based on the concept of dominant sets clustering. Dominant sets clustering is a graph-theoretic approach that has proven to work well in data clustering, and has been successfully adapted to topology estimation in this work. The proposed approach has been applied to three public databases (IOSTAR, INSPIRE, and VICAVR) and achieved high accuracy of 0.915, 0.928, and 0.889 respectively. The experimental results show that it has effectively addressed crossover problem that is the bottleneck issue in reconstruct vascular topology. It is also worth noting that we have made manual annotations of vessel topologies from these databases, and these annotations will be released soon.

Yalin Zheng

Papers

A compactness based saliency approach for leakages detection in fluorescein angiogram

Automatic Detection and Identification of Retinal Vessel Junctions in Colour Fundus Photography

Feature visualisation of classification of diabetic retinopathy using a convolutional neural network

Retinal blood vessel detection using multiscale line filter and phase congruency

Retinal vascular topology estimation via dominant sets clustering