We propose a novel method based on sparse representation for breast ultrasound image classification under the framework of multi-instance learning (MIL). After image enhancement and segmentation, concentric circle is used to extract the global and local features for improving the accuracy in diagnosis and prediction. The classification problem of ultrasound image is converted to sparse represen...

NK cells require an intact bone marrow microenvironment to acquire lytic function. In mice rendered osteopetrotic by 17beta-estradiol treatment, NK1.1 positive cells are arrested in a nonlytic state. Culture with as little as 2 ng/ml of murine IL-15 (mIL-15), a cytokine produced by macrophages and stromal cells, causes these immature NK1.1+ cells to acquire lytic activity. By contrast, approxim...

In this paper we demonstrate how deterministic annealing can be applied to different SVM formulations of the multiple-instance learning (MIL) problem. Our results show that we find better local minima compared to the heuristic methods those problems are usually solved with. However this does not always translate into a better test error suggesting an inadequacy of the objective function. Based ...

Introduction: MIL 03346 is a newly discovered clinopyroxenite belonging to the nakhlite class of SNC (martian) meteorites [1]. Although the petrographic and geochemical characteristics of MIL 03346 are broadly similar to those of previously known nakhlites, there are also some differences. In particular, it has the highest abundance of glassy mesostasis among the nakhlites, possibly indicative ...

This paper establishes a link between two supervised learning frameworks, namely multiple-instance learning (MIL) and learning from only positive and unlabelled examples (LOPU). MIL represents an object as a bag of instances. It is studied under the assumption that its instances are drawn from a mixture distribution of the concept and the non-concept. Based on this assumption, the classificatio...

In this paper the effectiveness of a corrective learning algorithm MIL (Mirror Image Learning) [1], [2] is comparatively studied with that of GLVQ (Generalized Learning Vector Quantization) [3]. Both MIL and GLVQ were proposed to improve the learning effectiveness beyond the limitation due to independent estimation of class conditional distributions. While the GLVQ modifies the representative v...

Multi-Instance Learning(MIL) performs well to deal with inherently ambiguity of images in multimedia retrieval. In this paper, an effective framework for Contented-Based Image Retrieval(CBIR) with MIL techniques is proposed, the effective mechanism is based on the image segmentation employing improved Mean Shift algorithm, and processes the segmentation results utilizing mathematical morphology...

Presenter: Lewis Gray Track: Track 6 Day: Wednesday

The present paper aims to investigate the role of open metal site metal-organic frameworks (MOFs) on hydrogen adsorptivity using periodic boundary condition (PBC) density functional theory (DFT). Hence, MIL-47-M (M = V and Fe) were selected and one hydrogen molecule adsorptivity was calculated in different orientations on them. Four different chemical sites were identified in every cluster sect...

We present MI-CRF, a conditional random field (CRF) model for multiple instance learning (MIL). MI-CRF models bags as nodes in a CRF with instances as their states. It combines discriminative unary instance classifiers and pairwise dissimilarity measures. We show that both forces improve the classification performance. Unlike other approaches, MI-CRF considers all bags jointly during training a...