2,537 research outputs found
Letter to the Editor
The paper by Alfons, Croux and Gelper (2013), Sparse least trimmed squares
regression for analyzing high-dimensional large data sets, considered a
combination of least trimmed squares (LTS) and lasso penalty for robust and
sparse high-dimensional regression. In a recent paper [She and Owen (2011)], a
method for outlier detection based on a sparsity penalty on the mean shift
parameter was proposed (designated by "SO" in the following). This work is
mentioned in Alfons et al. as being an "entirely different approach." Certainly
the problem studied by Alfons et al. is novel and interesting.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS640 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading
Diabetic retinopathy (DR) is a common retinal disease that leads to
blindness. For diagnosis purposes, DR image grading aims to provide automatic
DR grade classification, which is not addressed in conventional research
methods of binary DR image classification. Small objects in the eye images,
like lesions and microaneurysms, are essential to DR grading in medical
imaging, but they could easily be influenced by other objects. To address these
challenges, we propose a new deep learning architecture, called BiRA-Net, which
combines the attention model for feature extraction and bilinear model for
fine-grained classification. Furthermore, in considering the distance between
different grades of different DR categories, we propose a new loss function,
called grading loss, which leads to improved training convergence of the
proposed approach. Experimental results are provided to demonstrate the
superior performance of the proposed approach.Comment: Accepted at ICIP 201
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