Localization Recall Precision (LRP): A New Performance Metric for Object Detection

Average precision (AP), the area under the recall-precision (RP) curve, is the standard performance measure for object detection. Despite its wide acceptance, it has a number of shortcomings, the most important of which are (i) the inability to distinguish very different RP curves, and (ii) the lack of directly measuring bounding box localization accuracy. In this paper, we propose 'Localization Recall Precision (LRP) Error', a new metric which we specifically designed for object detection. LRP Error is composed of three components related to localization, false negative (FN) rate and false positive (FP) rate. Based on LRP, we introduce the 'Optimal LRP', the minimum achievable LRP error representing the best achievable configuration of the detector in terms of recall-precision and the tightness of the boxes. In contrast to AP, which considers precisions over the entire recall domain, Optimal LRP determines the 'best' confidence score threshold for a class, which balances the trade-off between localization and recall-precision. In our experiments, we show that, for state-of-the-art object (SOTA) detectors, Optimal LRP provides richer and more discriminative information than AP. We also demonstrate that the best confidence score thresholds vary significantly among classes and detectors. Moreover, we present LRP results of a simple online video object detector which uses a SOTA still image object detector and show that the class-specific optimized thresholds increase the accuracy against the common approach of using a general threshold for all classes. At https://github.com/cancam/LRP we provide the source code that can compute LRP for the PASCAL VOC and MSCOCO datasets. Our source code can easily be adapted to other datasets as well.Comment: to appear in ECCV 201

Local and Regional Food Aid Procurement: An Assessment of Experience in Africa and Elements of Good Donor Practice

This Policy Synthesis is a summary of a longer report that discusses the procurement of food aid within the country or region where it is needed. Referred to as local and regional procurement – LRP – this practice has become a major element in multilateral food aid response over the past decade1. The paper examines the relevance and the rationale for using LRP, reviews the efficiency of World Food Program (WFP) LRP activities in Africa relative to inkind food aid and to prices in the markets in which it occurs, and proposes a classification of risks in LRP. It then discusses a range of potential LRP modalities, and proposes a framework of guiding principles, information systems, and operational procedures for responsible and effective LRP. Finally, the paper briefly discusses the implications of this research for expansion of U.S. government (USG) authority to engage in LRP.Crop Production/Industries, Farm Management,

Inhibition of Shedding of Low-Density Lipoprotein Receptor-Related Protein 1 Reverses Cartilage Matrix Degradation in Osteoarthritis

OBJECTIVE: The aggrecanase ADAMTS-5 and the collagenase matrix metalloproteinase 13 (MMP-13) are constitutively secreted by chondrocytes in normal cartilage, but rapidly endocytosed via the cell surface endocytic receptor low-density lipoprotein receptor-related protein 1 (LRP-1) and subsequently degraded. This endocytic system is impaired in osteoarthritic (OA) cartilage due to increased ectodomain shedding of LRP-1. The aim of this study was to identify the LRP-1 sheddase(s) in human cartilage and to test whether inhibition of LRP-1 shedding prevents cartilage degradation in OA. METHODS: Cell-associated LRP-1 and soluble LRP-1 (sLRP-1) released from human cartilage explants and chondrocytes were measured by Western blot analysis. LRP-1 sheddases were identified by proteinase inhibitor profiling and gene silencing with small interfering RNAs. Specific monoclonal antibodies were used to selectively inhibit the sheddases. Degradation of aggrecan and collagen in human OA cartilage was measured by Western blot analysis using an antibody against an aggrecan neoepitope and a hydroxyproline assay, respectively. RESULTS: Shedding of LRP-1 was increased in OA cartilage compared with normal tissue. Shed sLRP-1 bound to ADAMTS-5 and MMP-13 and prevented their endocytosis without interfering with their proteolytic activities. Two membrane-bound metalloproteinases, ADAM-17 and MMP-14, were identified as the LRP-1 sheddases in cartilage. Inhibition of their activities restored the endocytic capacity of chondrocytes and reduced degradation of aggrecan and collagen in OA cartilage. CONCLUSION: Shedding of LRP-1 is a key link to OA progression. Local inhibition of LRP-1 sheddase activities of ADAM-17 and MMP-14 is a unique way to reverse matrix degradation in OA cartilage and could be effective as a therapeutic approach

Layer-Wise Relevance Propagation for Explaining Deep Neural Network Decisions in MRI-Based Alzheimer's Disease Classification

Deep neural networks have led to state-of-the-art results in many medical imaging tasks including Alzheimer’s disease (AD) detection based on structural magnetic resonance imaging (MRI) data. However, the network decisions are often perceived as being highly non-transparent, making it difficult to apply these algorithms in clinical routine. In this study, we propose using layer-wise relevance propagation (LRP) to visualize convolutional neural network decisions for AD based on MRI data. Similarly to other visualization methods, LRP produces a heatmap in the input space indicating the importance/relevance of each voxel contributing to the final classification outcome. In contrast to susceptibility maps produced by guided backpropagation (“Which change in voxels would change the outcome most?”), the LRP method is able to directly highlight positive contributions to the network classification in the input space. In particular, we show that (1) the LRP method is very specific for individuals (“Why does this person have AD?”) with high inter-patient variability, (2) there is very little relevance for AD in healthy controls and (3) areas that exhibit a lot of relevance correlate well with what is known from literature. To quantify the latter, we compute size-corrected metrics of the summed relevance per brain area, e.g., relevance density or relevance gain. Although these metrics produce very individual “fingerprints” of relevance patterns for AD patients, a lot of importance is put on areas in the temporal lobe including the hippocampus. After discussing several limitations such as sensitivity toward the underlying model and computation parameters, we conclude that LRP might have a high potential to assist clinicians in explaining neural network decisions for diagnosing AD (and potentially other diseases) based on structural MRI data

Expression of LDL receptor-related proteins (LRPs) in common solid malignancies correlates with patient survival

LDL receptor-related proteins (LRPs) are transmembrane receptors involved in endocytosis, cell-signaling, and trafficking of other cellular proteins. Considerable work has focused on LRPs in the fields of vascular biology and neurobiology. How these receptors affect cancer progression in humans remains largely unknown. Herein, we mined provisional data-bases in The Cancer Genome Atlas (TCGA) to compare expression of thirteen LRPs in ten common solid malignancies in patients. Our first goal was to determine the abundance of LRP mRNAs in each type of cancer. Our second goal was to determine whether expression of LRPs is associated with improved or worsened patient survival. In total, data from 4,629 patients were mined. In nine of ten cancers studied, the most abundantly expressed LRP was LRP1; however, a correlation between LRP1 mRNA expression and patient survival was observed only in bladder urothelial carcinoma. In this malignancy, high levels of LRP1 mRNA were associated with worsened patient survival. High levels of LDL receptor (LDLR) mRNA were associated with decreased patient survival in pancreatic adenocarcinoma. High levels of LRP10 mRNA were associated with decreased patient survival in hepatocellular carcinoma, lung adenocarcinoma, and pancreatic adenocarcinoma. LRP2 was the only LRP for which high levels of mRNA expression correlated with improved patient survival. This correlation was observed in renal clear cell carcinoma. Insights into LRP gene expression in human cancers and their effects on patient survival should guide future research

Genetic algorithm for the continuous location-routing problem

This paper focuses on the continuous location-routing problem that comprises of the location of multiple depots from a given region and determining the routes of vehicles assigned to these depots. The objective of the problem is to design the delivery system of depots and routes so that the total cost is minimal. The standard location-routing problem considers a finite number of possible locations. The continuous location-routing problem allows location to infinite number of locations in a given region and makes the problem much more complex. We present a genetic algorithm that tackles both location and routing subproblems simultaneously.Web of Science29318717