9 research outputs found
Few-shot Diagnosis of Chest x-rays Using an Ensemble of Random Discriminative Subspaces
Due to the scarcity of annotated data in the medical domain, few-shot
learning may be useful for medical image analysis tasks. We design a few-shot
learning method using an ensemble of random subspaces for the diagnosis of
chest x-rays (CXRs). Our design is computationally efficient and almost 1.8
times faster than method that uses the popular truncated singular value
decomposition (t-SVD) for subspace decomposition. The proposed method is
trained by minimizing a novel loss function that helps create well-separated
clusters of training data in discriminative subspaces. As a result, minimizing
the loss maximizes the distance between the subspaces, making them
discriminative and assisting in better classification. Experiments on
large-scale publicly available CXR datasets yield promising results. Code for
the project will be available at
https://github.com/Few-shot-Learning-on-chest-x-ray/fsl_subspace.Comment: ICLR MLGH Workshop 202
Non-locally Enhanced Encoder-Decoder Network for Single Image De-raining
Single image rain streaks removal has recently witnessed substantial progress
due to the development of deep convolutional neural networks. However, existing
deep learning based methods either focus on the entrance and exit of the
network by decomposing the input image into high and low frequency information
and employing residual learning to reduce the mapping range, or focus on the
introduction of cascaded learning scheme to decompose the task of rain streaks
removal into multi-stages. These methods treat the convolutional neural network
as an encapsulated end-to-end mapping module without deepening into the
rationality and superiority of neural network design. In this paper, we delve
into an effective end-to-end neural network structure for stronger feature
expression and spatial correlation learning. Specifically, we propose a
non-locally enhanced encoder-decoder network framework, which consists of a
pooling indices embedded encoder-decoder network to efficiently learn
increasingly abstract feature representation for more accurate rain streaks
modeling while perfectly preserving the image detail. The proposed
encoder-decoder framework is composed of a series of non-locally enhanced dense
blocks that are designed to not only fully exploit hierarchical features from
all the convolutional layers but also well capture the long-distance
dependencies and structural information. Extensive experiments on synthetic and
real datasets demonstrate that the proposed method can effectively remove
rain-streaks on rainy image of various densities while well preserving the
image details, which achieves significant improvements over the recent
state-of-the-art methods.Comment: Accepted to ACM Multimedia 201
Low Packet Loss and High PDR based Self Adaptive Sleep Wake Scheduling Technique for WSN
Abstract— Wireless Sensor Networks consisting ofnodes with limited power are deployed to gatheruseful information from the field. In WSNs it iscritical to collect the information in an efficientmanner. It is applied in routing and difficult powersupply area that cannot be reached and sometemporary situations, which do not need fixednetwork supporting and it can fast deploy with stronganti-damage. In order to avoid the problem, weproposed a new technique called Bio-Inspiredmechanism for routing. Proposed algorithm showsbetter performance in terms of Packet Loss andDelay