Divide and conquer: A hierarchical approach to large-scale structure-from-motion

In this paper we present a novel pipeline for large-scale SfM. We first organise the images into a hierarchical tree built using agglomerative clustering. The SfM problem is then solved by reconstructing smaller image sets and merging them into a common frame of reference as we move up the tree in a bottom-up fashion. Such an approach drastically reduces the computational load for matching image pairs without sacrificing accuracy. It also makes the resulting sequence of bundle adjustment problems well-conditioned at all stages of reconstruction. We use motion averaging followed by global bundle adjustment for reconstruction of each individual cluster. Our 3D registration or alignment of partial reconstructions based on epipolar relationships is both robust and reliable and works well even when the available camera-point relationships are poorly conditioned. The overall result is a robust, accurate and efficient pipeline for large-scale SfM. We present extensive results that demonstrate these attributes of our pipeline on a number of large-scale, real-world datasets and compare with the state-of-the-art. (C) 2017 Elsevier Inc. All rights reserved

Divide and Conquer: Efficient Large-Scale Structure from Motion Using Graph Partitioning

Despite significant advances in recent years, structure-from-motion (SfM) pipelines suffer from two important drawbacks. Apart from requiring significant computational power to solve the large-scale computations involved, such pipelines sometimes fail to correctly reconstruct when the accumulated error in incremental reconstruction is large or when the number of 3D to 2D correspondences are insufficient. In this paper we present a novel approach to mitigate the above-mentioned drawbacks. Using an image match graph based on matching features we partition the image data set into smaller sets or components which are reconstructed independently. Following such reconstructions we utilise the available epipolar relationships that connect images across components to correctly align the individual reconstructions in a global frame of reference. This results in both a significant speed up of at least one order of magnitude and also mitigates the problems of reconstruction failures with a marginal loss in accuracy. The effectiveness of our approach is demonstrated on some large-scale real world data sets

DeepSGP: Deep Learning for Gene Selection and Survival Group Prediction in Glioblastoma

Glioblastoma Multiforme (GBM) is an aggressive form of glioma, exhibiting very poor survival. Genomic input, in the form of RNA sequencing data (RNA-seq), is expected to provide vital information about the characteristics of the genes that affect the Overall Survival (OS) of patients. This could have a significant impact on treatment planning. We present a new Autoencoder (AE)-based strategy for the prediction of survival (low or high) of GBM patients, using the RNA-seq data of 129 GBM samples from The Cancer Genome Atlas (TCGA). This is a novel interdisciplinary approach to integrating genomics with deep learning towards survival prediction. First, the Differentially Expressed Genes (DEGs) were selected using EdgeR. These were further reduced using correlation-based analysis. This was followed by the application of ranking with different feature subset selection and feature extraction algorithms, including the AE. In each case, fifty features were selected/extracted, for subsequent prediction with different classifiers. An exhaustive study for survival group prediction, using eight different classifiers with the accuracy and Area Under the Curve (AUC), established the superiority of the AE-based feature extraction method, called DeepSGP. It produced a very high accuracy (0.83) and AUC (0.90). Of the eight classifiers, using the extracted features by DeepSGP, the MLP was the best at Overall Survival (OS) prediction with an accuracy of 0.89 and an AUC of 0.97. The biological significance of the genes extracted by the AE were also analyzed to establish their importance. Finally, the statistical significance of the predicted output of the DeepSGP algorithm was established using the concordance index

Increased levels of interleukin‐10 and IgG3 are hallmarks of Indian Post–kala-azar dermal leishmaniasis

Background. Post-kala-azar dermal leishmaniasis (PKDL), an established sequela of visceral leishmaniasis (VL), is proposed to facilitate anthroponotic transmission of VL, especially during interepidemic periods. Immunopathological mechanisms responsible for Indian PKDL are still poorly defined. Methods. Our study attempted to characterize the immune profiles of patients with PKDL or VL relative to that of healthy control subjects by immunophenotyping, intracellular cytokine staining of peripheral blood mononuclear cells, and enzyme-linked immunosorbent assay for serum cytokines and immunoglobulin G (IgG) subclasses. Results. Patients with PKDL had significantly raised percentages of peripheral CD3+CD8+ cells compared with control subjects, a difference that persisted after cure. Patients with PKDL showed an intact response to phytohemagglutinin, with the percentages of lymphocytes expressing interferon (IFN)-γ, interleukin (IL)-2, IL-4, and IL-10 being comparable to those in control subjects. Patients with VL had decreased IFN-γ and IL-2 expression, which was restored after cure, and increased IL-10 expression, which persisted after cure. In their response to Leishmania donovani antigen, patients with PKDL showed a 9.6-fold increase in the percentage of IL-10-expressing CD3+CD8+ lymphocytes compared with control subjects, and this percentage decreased with treatment. Patients with PKDL had raised levels of IgG3 and IgG1 (surrogate markers for IL-10), concomitant with increased serum levels of IL-10. Conclusions. IL-10-producing CD3+CD8+ lymphocytes are important protagonists in the immunopathogenesis of Indian PKDL