Learned versus Hand-Designed Feature Representations for 3d Agglomeration

For image recognition and labeling tasks, recent results suggest that machine learning methods that rely on manually specified feature representations may be outperformed by methods that automatically derive feature representations based on the data. Yet for problems that involve analysis of 3d objects, such as mesh segmentation, shape retrieval, or neuron fragment agglomeration, there remains a strong reliance on hand-designed feature descriptors. In this paper, we evaluate a large set of hand-designed 3d feature descriptors alongside features learned from the raw data using both end-to-end and unsupervised learning techniques, in the context of agglomeration of 3d neuron fragments. By combining unsupervised learning techniques with a novel dynamic pooling scheme, we show how pure learning-based methods are for the first time competitive with hand-designed 3d shape descriptors. We investigate data augmentation strategies for dramatically increasing the size of the training set, and show how combining both learned and hand-designed features leads to the highest accuracy

Machine learning of image analysis with convolutional networks and topological constraints

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 130-140).We present an approach to solving computer vision problems in which the goal is to produce a high-dimensional, pixel-based interpretation of some aspect of the underlying structure of an image. Such tasks have traditionally been categorized as ''low-level vision'' problems, and examples include image denoising, boundary detection, and motion estimation. Our approach is characterized by two main elements, both of which represent a departure from previous work. The first is a focus on convolutional networks, a machine learning strategy that operates directly on an input image with no use of hand-designed features and employs many thousands of free parameters that are learned from data. Previous work in low-level vision has been largely focused on completely hand-designed algorithms or learning methods with a hand-designed feature space. We demonstrate that a learning approach with high model complexity, but zero prior knowledge about any specific image domain, can outperform existing techniques even in the challenging area of natural image processing. We also present results that establish how convolutional networks are closely related to Markov random fields (MRFs), a popular probabilistic approach to image analysis, but can in practice can achieve significantly greater model complexity. The second aspect of our approach is the use of domain specific cost functions and learning algorithms that reflect the structured nature of certain prediction problems in image analysis.(cont.) In particular, we show how concepts from digital topology can be used in the context of boundary detection to both evaluate and optimize the high-order property of topological accuracy. We demonstrate that these techniques can significantly improve the machine learning approach and outperform state of the art boundary detection and segmentation methods. Throughout our work we maintain a special interest and focus on application of our methods to connectomics, an emerging scientific discipline that seeks high-throughput methods for recovering neural connectivity data from brains. This application requires solving low-level image analysis problems on a tera-voxel or peta-voxel scale, and therefore represents an extremely challenging and exciting arena for the development of computer vision methods.by Viren Jain.Ph.D

Role of fiberoptic bronchoscopy in sputum smear negative suspected cases of pulmonary tuberculosis: a study conducted in Southern part of Rajasthan

Background: Sputum smear negative pulmonary tuberculosis is a common problem faced by clinicians. Fiberoptic bronchoscopy may be very useful in diagnosing these cases which have no sputum or whose sputum smear is negative for acid fast bacilli. Objective of the current study was to assess the role of fiberoptic bronchoscopy in sputum smear negative under NTEP and radiologically suspected cases of pulmonary tuberculosis.Methods: Clinico-radiological suspected cases of pulmonary tuberculosis patients in whom two sputum smear for acid fast bacilli by Ziehl Neelsen stain under NTEP was negative were included in the study. Fiberoptic bronchoscopy was performed in all these patients and samples taken were sent for investigations.Results: Fiberoptic bronchoscopy was performed in 250 patients of suspected pulmonary tuberculosis whose sputum for AFB smear was negative. Cough was the most predominant symptom. Radiologically, right side disease was more common and upper zone was most commonly involved and infiltrates were common radiological finding. During bronchoscopy, congestion and hyperaemia (36%) and mucopurulent/mucoid secretions (32%) was seen in maximum number of cases. BAL was positive in 200 patients (80%), post bronchoscopy sputum was positive in 70 cases (28%) and biopsy was positive in 12 patients out of 16 performed biopsies (75%). The total TB positive cases after combining all the methods were 215 making the overall diagnostic yield of 86%.Conclusions: Fiberoptic bronchoscopy and post bronchoscopy sputum can be very useful for diagnosing sputum for AFB smear negative but clinico-radiological suspected cases of pulmonary tuberculosis patients

A study of bronchial asthma in school going children in Southern part of Rajasthan

Background: Asthma is a chronic and common inflammatory disease involving mainly large airways of lungs. Childhood asthma is common chronic illness among school going children and is usually underdiagnosed and undertreated. The aim of the present study was to find out of the prevalence of Bronchial asthma in school going children of age group 6-12 years in southern part of Rajasthan (India), and its relation with gender, socio-economic status and heredity.Methods: A questionnaire-based study has been carried out in 1500 children of 6 to 12 years age group in four schools of Udaipur city (Rajasthan, India) with a response rate of 60.23% (904/1500).Results: The overall prevalence of asthma observed is 4.75% (43/904). The prevalence is higher among boys (5.55%) as compared to girls (3.75%). Further the prevalence is higher in upper (7.18%) and upper middle class (7.14%) children as compared to lower middle (4.84%) and upper lower class (2.01%) socioeconomic status. The children with positive family history of asthma also have higher prevalence (26.31%) of asthma.Conclusions: The prevalence of childhood asthma in Udaipur city is relatively lower and supports the already reported relation with gender, socioeconomic status and heredity.

A connectome and analysis of the adult Drosophila central brain.

The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly's brain