Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

A general non-parametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure: the mean shift. For discrete data, we prove the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its utility in detecting the modes of the density. The relation of the mean shift procedure to the Nadaraya-Watson estimator from kernel regression and the robust M-estimators; of location is also established. Algorithms for two low-level vision tasks discontinuity-preserving smoothing and image segmentation - are described as applications. In these algorithms, the only user-set parameter is the resolution of the analysis, and either gray-level or color images are accepted as input. Extensive experimental results illustrate their excellent performance.

Mean shift: a robust approach toward feature space analysis

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

A survey on deep learning in medical image analysis

Computer vision applications have come to rely increasingly on superpixels in recent years, but it is not always clear what constitutes a good superpixel algorithm. In an effort to understand the benefits and drawbacks of existing methods, we empirically compare five state-of-the-art superpixel algorithms for their ability to adhere to image boundaries, speed, memory efficiency, and their impact on segmentation performance. We then introduce a new superpixel algorithm, simple linear iterative clustering (SLIC), which adapts a k-means clustering approach to efficiently generate superpixels. Despite its simplicity, SLIC adheres to boundaries as well as or better than previous methods. At the same time, it is faster and more memory efficient, improves segmentation performance, and is straightforward to extend to supervoxel generation.

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SLIC Superpixels Compared to State-of-the-Art Superpixel Methods

In this paper we introduce a non-iterative algorithm for vector quantization clustering based on the efficient search for the two clusters whose merging gives the minimum distortion increase. The search is performed within the A'-dimensional cells of a lattice having a generating matrix that changes from one step of the algorithm to another. The generating matrix is modified gradually so that the lattice cells grow in volume, allowing the search of the two closest clusters in an enlarged neighborhood. We call this algorithm Lattice Growing Search (LGS) clustering. Preliminary results on 512 × 512 images encoded at 0.5 bits/pixel showed that the LGS technique can produce codebooks of similar quality in less than 1/10 of the time required by the LBG algorithm [9].

Vector quantization clustering using lattice growing search

Systems and methods are provided for classifying an abnormality in a medical image. An input medical image depicting a lesion is received. The lesion is localized in the input medical image using a trained localization network to generate a localization map. The lesion is classified based on the input medical image and the localization map using a trained classification network. The classification of the lesion is output. The trained localization network and the trained classification network are jointly trained.

Localization and classification of abnormalities in medical images

Es werden ein Verfahren und ein System zur automatischen nicht-invasiven Abschatzung von Schermodul und Viskositat von biologischem Gewebe aus Scherwellen-Bildgebung offenbart. Es werden Scherwellen-Bilder erfasst, um die mechanischen Eigenschaften des Gewebes eines Patienten abzuschatzen. Die Scherwellen-Ausbreitung in dem Gewebe in den Scherwellen-Bildern wird auf der Grundlage von Schermodul- und Viskositatswerten fur das Gewebe unter Verwendung eines rechnerischen Modells der Scherwellen-Ausbreitung simuliert. Die simulierte Scherwellen-Ausbreitung wird unter Verwendung einer Kostenfunktion mit der beobachteten Scherwellen-Ausbreitung in den Scherwellen-Bildern des Gewebes verglichen. Patientenspezifische Schermodul- und Viskositatswerte fur das Gewebe werden abgeschatzt, um die Kostenfunktion, welche die simulierte Scherwellen-Ausbreitung mit der beobachteten Scherwellen-Ausbreitung vergleicht, zu optimieren.

Verfahren und System zur automatischen Abschätzung von Schermodul und Viskosität aus Scherwellen-Bildgebung

Reply to Liu et al.

Recent technological advances in deep learning (DL) have led to more accurate brain metastasis (BM) detection. As a data driven approach, DL’s performance highly relies on the size and quality of the training data. However, collecting large amount of medical data is costly, and it’s difficult to include BMs with various locations, sizes, and structures etc. Thus, we propose a 3D-2D GAN for fully 3D BM synthesis with configurable parameters. First, two 3D networks are used to synthesize the mask and quantized intensity map of a lesion from 3 concentric spheres, which are used to control the lesion’s location, size and structure. Then, a 2D network is used to synthesize the final lesion with proper appearance from the quantized intensity map and the background MR image. With this 3D-2D design, the 3D networks enable the synthetic metastasis to be spatially continuous in all 3 dimensions through the guidance of the 3D intermediate presentation of the lesion, while the 2D network enables the use of 2D perceptual loss to make the final synthesized lesion look realistic. In addition, different network up-sampling strategies and postprocessing are used to control the heterogeneity and contrast of the synthetic lesion. All the synthesized images were reviewed by a radiologist. The indistinguishability rate of the synthesized lesion is above 70%. The configurable parameters for the lesion’s location, size, and structure, heterogeneity and contrast were reviewed to be effective. Our work demonstrates the feasibility of synthesizing configurable 3D BM lesions for fully 3D data augmentation.

Dorin Comaniciu

Papers

Vector quantization clustering using lattice growing search

Localization and classification of abnormalities in medical images

Verfahren und System zur automatischen Abschätzung von Schermodul und Viskosität aus Scherwellen-Bildgebung

Reply to Liu et al.

3D-2D GAN based brain metastasis synthesis with configurable parameters for fully 3D data augmentation