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

Image classification is a complex process that may be affected by many factors. This paper examines current practices, problems, and prospects of image classification. The emphasis is placed on the summarization of major advanced classification approaches and the techniques used for improving classification accuracy. In addition, some important issues affecting classification performance are discussed. This literature review suggests that designing a suitable image-processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map. Effective use of multiple features of remotely sensed data and the selection of a suitable classification method are especially significant for improving classification accuracy. Non-parametric classifiers such as neural network, decision tree classifier, and knowledge-based classification have increasingly become important approaches for multisource data classification. Integration of remote sensing, geographical information systems (GIS), and expert system emerges as a new research frontier. More research, however, is needed to identify and reduce uncertainties in the image-processing chain to improve classification accuracy.

/pdf/a-survey-of-image-classification-methods-and-techniques-for-5bc5ltr9dq.pdf

A survey of image classification methods and techniques for improving classification performance

A wide range of methods for analysis of airborne- and satellite-derived imagery continues to be proposed and assessed. In this paper, we review remote sensing implementations of support vector machines (SVMs), a promising machine learning methodology. This review is timely due to the exponentially increasing number of works published in recent years. SVMs are particularly appealing in the remote sensing field due to their ability to generalize well even with limited training samples, a common limitation for remote sensing applications. However, they also suffer from parameter assignment issues that can significantly affect obtained results. A summary of empirical results is provided for various applications of over one hundred published works (as of April, 2010). It is our hope that this survey will provide guidelines for future applications of SVMs and possible areas of algorithm enhancement.

https://www.aboutgis.com/Publications/Mountrakis_SVM_review_in_remote_sensing_ISPRS2010.pdf

Support vector machines in remote sensing: A review

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumoris a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses thestate-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross tota lresection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Radiomics is an emerging field in quantitative imaging that uses advanced imaging features to objectively and quantitatively describe tumour phenotypes. Radiomic features have recently drawn considerable interest due to its potential predictive power for treatment outcomes and cancer genetics, which may have important applications in personalized medicine. In this technical review, we describe applications and challenges of the radiomic field. We will review radiomic application areas and technical issues, as well as proper practices for the designs of radiomic studies.

/pdf/applications-and-limitations-of-radiomics-38sh1zf0rc.pdf

Applications and limitations of radiomics

Remote Sensing Image Classification: A Wavelet-Neuro-Fuzzy Approach

We present here an approach for video image segmentation where spatial segmentation is based on rough sets and granular computing and temporal segmentation is done by consecutive frame subtraction. Then the intersection of the temporal segmentation and spatial segmentation for the same frame is analyzed in RGB feature space. The estimated statistics of the intersecting regions is used for the object reconstruction and tracking.

/pdf/spatiotemporal-approach-for-tracking-using-rough-entropy-and-4vdee5z5vk.pdf

Spatiotemporal approach for tracking using rough entropy and frame subtraction

Diabetic Retinopathy (DR) is a major cause of blindness among people affected with diabetes for over 10 years. The two fundamental stages of DR are – Non-Proliferative Diabetic Retinopathy and Proliferative Diabetic Retinopathy. The progress from the former to the latter takes time and early detection can prevent blindness. The early symptoms of DR include Red Lesions which constitute microaneurysms and hemorrhages. This paper targets to detect red lesions at the preliminary stage of DR and also in the later stage. The work implements two state-of-the-art models to detect microaneurysms and hemorrhages. The results obtained show that RetinaNet used, performs better than Faster RCNN with the publicly available dataset Messidor.

Detection of Red Lesions in Diabetic Retinopathy using Deep Learning

xiv 1 Chapter 1 1 1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 What did we do? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Related Work Reviewing previous literature . . . . . . . . . . . . . . . . . . 3

Multi-Label Classification on Remote-Sensing Images

The present work analyses the effect of three spatial filters, modelling the psychophysics data on brightness perception, when applied to a difficult computer vision problem. This pertains to the automatic grain-matrix segmentation of cross-polar images of sedimentary rock thin sections, gathered through petrographic microscopy. These three filters are assumed to be modelling three modes of vision, viz. vision-at-a-glance (VG, through Magnocellular or M channel in primate visual pathway), vision-in-strong-contrast mode (VSC, through Parvocellular or P channel of visual pathway), and vision-with-scrutiny (VS, through Magno-Parvochannels combined referred as the PM filter). The aforementioned biologically inspired filters related to brightness–contrast modelling are applied on the cross-polar microscopic images. Then binary segmentation is performed on the original, as well as the filtered, images using a k-means clustering algorithm which effectively segment the input images according to the homogeneity present. The binary segmented output when compared with our manually built grain-matrix segmented ground truth using suitable indices, forecasts noticeable difference in terms of the applied filter on them and the grain size distribution in that image, manifested sometimes by the resolution scale of capturing the image. Mostly for images with higher resolution of microscope lens while capturing, or for images displaying larger grain size, the PM filtered image has shown better grain-matrix segmentation, though the effect of filtering is not very pronounced for such images. On the other hand, for lower resolution of microscope lens for capturing the image or for smaller grain size featured images, the P filtered image has consistently shown more accurate grain-matrix aggregation result as also distinct improvement over those not filtered. The novelty of the approach lies in the application of a psychophysics model that attempts to mimic the manual segmentation performed by the trained eye of the expert in various visual modes. The proposed method achieves a reasonably higher accuracy in grain segmentation when compared with relevant existing methods especially while dealing with more difficult images containing many small-sized grains.

B. Uma Shankar

Papers

Remote Sensing Image Classification: A Wavelet-Neuro-Fuzzy Approach

Spatiotemporal approach for tracking using rough entropy and frame subtraction

Detection of Red Lesions in Diabetic Retinopathy using Deep Learning

Multi-Label Classification on Remote-Sensing Images

Automatic grain segmentation in cross-polarized photomicrographs of sedimentary rocks using psychophysics inspired models