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.

/pdf/slic-superpixels-compared-to-state-of-the-art-superpixel-5d8sq04am9.pdf

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

Patient, user, and/or AI information are used in a multi-objective optimization to select one of a plurality of available AIs for a task. On a patient or user-specific basis, an optimal AI is selected and applied for medical imaging or other healthcare actions. The selection may be before application, avoiding costs of applying multiple AIs to obtain the best results. The optimization may be based on statistical feedback from the user for various of the available AIs, providing information not otherwise available. The optimization may be based on AI performance, AI inclusion and/or exclusion criteria, and/or pricing information. By using optimization based on various information related to the patient, user, and/or available AI, the application of AI for a given user and/or patient by the computer may be improved. The computer operates better to provide more focused information through AI application.

Artificial intelligence dispatch in healthcare

In response to the growing need for image analysis services in the cloud computing environment, this paper proposes an automatic system for detecting landmarks in 3D volumes. The inherent problem of limited bandwidth between a (thin) client, Data Center (DC), and Data Analysis (DA) server is addressed by a hierarchical detection algorithm that obtains data by progressively transmitting only image regions required for processing. The client sends a request for a visualization of a specific landmark. The algorithm obtains a coarse level image from DC and outputs landmark location candidates. The coarse landmark location candidates are then used to obtain image neighborhood regions at a finer resolution level. The final location is computed as the robust mean of the strongest candidates after refinement at the subsequent resolution levels. The feedback about candidates detected at a coarser resolution makes it possible to only transmit image regions surrounding these candidates at a finer resolution rather then the entire images. Furthermore, the image regions are lossy compressed with JPEG 2000. Together, these properties amount to at least 30 times bandwidth reduction while achieving similar accuracy when compared to an algorithm using the original data.

http://www.cs.rpi.edu/~sofka/pdfs/sofka-HPmiccai10.pdf

Progressive Data Transmission for Hierarchical Detection in a Cloud

A computer-implemented method for image-guided delivery of a nanoparticle mixture to a target tumor located in a region of interest includes selecting a non-hypoxic delivery location within the region of interest for delivery of a non-bacteria-associated nanoparticle component included in the nanoparticle mixture and selecting a hypoxic delivery location within the region of interest for delivery of a bacteria-associated nanoparticle component included in the nanoparticle mixture. An image-guided delivery and monitoring process may then be performed. During this process intra-operative images of the region of interest are continually acquired and used to guide placement of a device into the non-hypoxic delivery location, monitor delivery of the non-bacteria-associated nanoparticle component included in the nanoparticle mixture at the non-hypoxic delivery location, guide placement of the device into the hypoxic delivery location, and monitor delivery of the bacteria-associated nanoparticle component included in the nanoparticle mixture at the hypoxic delivery location.

Image-guided delivery of a mixture of bacteria and non-bacteria linked nanoparticles

Among all the sub-sections in a typical radiology report, the Clinical Indications, Findings, and Impression often reflect important details about the health status of a patient. The information included in Impression is also often covered in Findings. While Findings and Impression can be deduced by inspecting the image, Clinical Indications often require additional context. The cognitive task of interpreting medical images remains the most critical and often time-consuming step in the radiology workflow. Instead of generating an end-to-end radiology report, in this paper, we focus on generating the Findings from automated interpretation of medical images, specifically chest X-rays (CXRs). Thus, this work focuses on reducing the workload of radiologists who spend most of their time either writing or narrating the Findings. Unlike past research, which addresses radiology report generation as a single-step image captioning task, we have further taken into consideration the complexity of interpreting CXR images and propose a two-step approach: (a) detecting the regions with abnormalities in the image, and (b) generating relevant text for regions with abnormalities by employing a generative large language model (LLM). This two-step approach introduces a layer of interpretability and aligns the framework with the systematic reasoning that radiologists use when reviewing a CXR.

Generation of Radiology Findings in Chest X-Ray by Leveraging Collaborative Knowledge

Marginal Space Learning (MSL) has been extensively tested on multiple anatomical structure detection and segmentation problems in different medical imaging modalities. In this chapter, we provide a review of its applications in the published literature. We first review applications on “pure” detection problems, followed by those combining detection, segmentation, and tracking.

Dorin Comaniciu

Papers

Artificial intelligence dispatch in healthcare

Progressive Data Transmission for Hierarchical Detection in a Cloud

Image-guided delivery of a mixture of bacteria and non-bacteria linked nanoparticles

Generation of Radiology Findings in Chest X-Ray by Leveraging Collaborative Knowledge

Applications of Marginal Space Learning in Medical Imaging