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

The Theory of Communicative Action, Vol. 1, Reason and the Rationalization of Society

MRI-based medical image analysis for brain tumor studies is gaining attention in recent times due to an increased need for efficient and objective evaluation of large amounts of data. While the pioneering approaches applying automated methods for the analysis of brain tumor images date back almost two decades, the current methods are becoming more mature and coming closer to routine clinical application. This review aims to provide a comprehensive overview by giving a brief introduction to brain tumors and imaging of brain tumors first. Then, we review the state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas. The objective in the segmentation is outlining the tumor including its sub-compartments and surrounding tissues, while the main challenge in registration and modeling is the handling of morphological changes caused by the tumor. The qualities of different approaches are discussed with a focus on methods that can be applied on standard clinical imaging protocols. Finally, a critical assessment of the current state is performed and future developments and trends are addressed, giving special attention to recent developments in radiological tumor assessment guidelines.

/pdf/a-survey-of-mri-based-medical-image-analysis-for-brain-tumor-4mtl4fktmo.pdf

A survey of MRI-based medical image analysis for brain tumor studies

White matter hyperintensities (WMHs) in the brain are the consequence of cerebral small vessel disease, and can easily be detected on MRI. Over the past three decades, research has shown that the presence and extent of white matter hyperintense signals on MRI are important for clinical outcome, in terms of cognitive and functional impairment. Large, longitudinal population-based and hospital-based studies have confirmed a dose-dependent relationship between WMHs and clinical outcome, and have demonstrated a causal link between large confluent WMHs and dementia and disability. Adequate differential diagnostic assessment and management is of the utmost importance in any patient, but most notably those with incipient cognitive impairment. Novel imaging techniques such as diffusion tensor imaging might reveal subtle damage before it is visible on standard MRI. Even in Alzheimer disease, which is thought to be primarily caused by amyloid, vascular pathology, such as small vessel disease, may be of greater importance than amyloid itself in terms of influencing the disease course, especially in older individuals. Modification of risk factors for small vessel disease could be an important therapeutic goal, although evidence for effective interventions is still lacking. Here, we provide a timely Review on WMHs, including their relationship with cognitive decline and dementia.

White matter hyperintensities, cognitive impairment and dementia: an update.

The identification of Hate Speech in Social Media is of great importance and receives much attention in the text classification community. There is a huge demand for research for languages other than English. The HASOC track intends to stimulate development in Hate Speech for Hindi, German and English. Three datasets were developed from Twitter and Facebook and made available. Binary classification and more fine-grained subclasses were offered in 3 subtasks. For all subtasks, 321 experiments were submitted. The approaches used most often were LSTM networks processing word embedding input. The performance of the best system for identification of Hate Speech for English, Hindi, and German was a Marco-F1 score of 0.78, 0.81 and 0.61, respectively.

/pdf/overview-of-the-hasoc-track-at-fire-2019-hate-speech-and-27y6nbsevs.pdf

Overview of the HASOC track at FIRE 2019: Hate Speech and Offensive Content Identification in Indo-European Languages

http://webdocs.cs.ualberta.ca/~btap/Papers/bounding_box_CMIG.pdf.old

Quick detection of brain tumors and edemas: A bounding box method using symmetry

Image thresholding by variational minimax optimization

BACKGROUND AND PURPOSE: WM lesion segmentation is often performed with the use of subjective rating scales because manual methods are laborious and tedious; however, automated methods are now available. We compared the performance of total lesion volume grading computed by use of an automated WM lesion segmentation algorithm with that of subjective rating scales and expert manual segmentation in a cohort of subjects with type 2 diabetes. MATERIALS AND METHODS: Structural T1 and FLAIR MR imaging data from 50 subjects with diabetes (age, 67.7 ± 7.2 years) and 50 nondiabetic sibling pairs (age, 67.5 ± 9.4 years) were evaluated in an institutional review board–approved study. WM lesion segmentation maps and total lesion volume were generated for each subject by means of the Statistical Parametric Mapping (SPM8) Lesion Segmentation Toolbox. Subjective WM lesion grade was determined by means of a 0–9 rating scale by 2 readers. Ground-truth total lesion volume was determined by means of manual segmentation by experienced readers. Correlation analyses compared manual segmentation total lesion volume with automated and subjective evaluation methods. RESULTS: Correlation between average lesion segmentation and ground-truth total lesion volume was 0.84. Maximum correlation between the Lesion Segmentation Toolbox and ground-truth total lesion volume (ρ = 0.87) occurred at the segmentation threshold of k = 0.25, whereas maximum correlation between subjective lesion segmentation and the Lesion Segmentation Toolbox (ρ = 0.73) occurred at k = 0.15. The difference between the 2 correlation estimates with ground-truth was not statistically significant. The lower segmentation threshold (0.15 versus 0.25) suggests that subjective raters overestimate WM lesion burden. CONCLUSIONS: We validate the Lesion Segmentation Toolbox for determining total lesion volume in diabetes-enriched populations and compare it with a common subjective WM lesion rating scale. The Lesion Segmentation Toolbox is a readily available substitute for subjective WM lesion scoring in studies of diabetes and other populations with changes of leukoaraiosis.

http://www.ajnr.org/content/ajnr/34/12/2265.full.pdf

Automated White Matter Total Lesion Volume Segmentation in Diabetes

In this paper we propose a locally adaptive image threshold technique via variational energy minimization. The novelty of the proposed method is that from an image it automatically computes the weights on the data fidelity and the regularization terms in the energy functional, unlike many other previously proposed variational formulations that require manual input of these weights by laborious trial and error. To achieve the automatic setting of the weighting parameters we propose a non-linear convex combination of the data fidelity and the regularization terms in the energy functional and seek the optimum threshold surface via minimax principle. Our choice of the novel energy functional allows fast computation of the unique minimax solution. As a specific segmentation application, the proposed technique shows promising results when applied to find lung boundary from MR imagery. Illustrative examples are also provided where the proposed method is observed to retain texture information better than other competing methods.

/pdf/edge-sensitive-variational-image-thresholding-4kqaz8skze.pdf

Edge Sensitive Variational Image Thresholding

Tumor/abnormality segmentation from magnetic resonance imagery (MRI) can play a significant role in cancer research and clinical practice Although accurate tumor segmentation by radiologists is ideal, it is extremely tedious Experience shows that for MRI database indexing purposes approximate segmentations can be adequate In this paper, we propose a straightforward, real-time technique to find a bounding box around the brain abnormality in an MR image Our algorithm exploits left-to-right symmetry of the brain structure The proposed detection algorithm can play a useful role in indexing and storage of bulk MRI data, as well as provide an initial step or seed to assist algorithms designed to find accurate tumor boundaries

https://webdocs.cs.ualberta.ca/~btap/Papers/Nilanjan_Asilomar2007.pdf

Baidya Nath Saha

Papers

Quick detection of brain tumors and edemas: A bounding box method using symmetry

Image thresholding by variational minimax optimization

Automated White Matter Total Lesion Volume Segmentation in Diabetes

Edge Sensitive Variational Image Thresholding

Locating Brain Tumors from MR Imagery Using Symmetry