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Found 35 papers, 12 papers with code
Confidence Dimension for Deep Learning based on Hoeffding Inequality and Relative Evaluation
Research on the generalization ability of deep neural networks (DNNs) has recently attracted a great deal of attention.
Adversarial Contrastive Self-Supervised Learning
Recently, learning from vast unlabeled data, especially self-supervised learning, has been emerging and attracted widespread attention.
Associative Adversarial Learning Based on Selective Attack
We show that our method improves the recognition accuracy of adversarial training on ImageNet by 8. 32% compared with the baseline.
MoCaNet: Motion Retargeting in-the-wild via Canonicalization Networks
Trained with the canonicalization operations and the derived regularizations, our method learns to factorize a skeleton sequence into three independent semantic subspaces, i. e., motion, structure, and view angle.
Self-Supervised Monocular Depth and Ego-Motion Estimation in Endoscopy: Appearance Flow to the Rescue
Recently, self-supervised learning technology has been applied to calculate depth and ego-motion from monocular videos, achieving remarkable performance in autonomous driving scenarios.
NENet: Monocular Depth Estimation via Neural Ensembles
In particular, our method improves previous state-of-the-art methods from 0. 365 to 0. 349 on the metric RMSE on the NYU dataset.
Joint Channel and Weight Pruning for Model Acceleration on Moblie Devices
In this paper, we present a unified framework with Joint Channel pruning and Weight pruning (JCW), and achieves a better Pareto-frontier between the latency and accuracy than previous model compression approaches.
SpeechNAS: Towards Better Trade-off between Latency and Accuracy for Large-Scale Speaker Verification
Recently, x-vector has been a successful and popular approach for speaker verification, which employs a time delay neural network (TDNN) and statistics pooling to extract speaker characterizing embedding from variable-length utterances.
Shifted Chunk Transformer for Spatio-Temporal Representational Learning
However, the pure-Transformer based spatio-temporal learning can be prohibitively costly on memory and computation to extract fine-grained features from a tiny patch.
Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures
Building robust deep learning-based models requires diverse training data, ideally from several sources.
Test-Time Training for Deformable Multi-Scale Image Registration
Registration is a fundamental task in medical robotics and is often a crucial step for many downstream tasks such as motion analysis, intra-operative tracking and image segmentation.
Deformable Gabor Feature Networks for Biomedical Image Classification
In recent years, deep learning has dominated progress in the field of medical image analysis.
Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan
To facilitate CT analysis, recent efforts have focused on computer-aided characterization and diagnosis, which has shown promising results.
Cycle-Consistent Adversarial Autoencoders for Unsupervised Text Style Transfer
Unsupervised text style transfer is full of challenges due to the lack of parallel data and difficulties in content preservation.
Multi-Domain Image Completion for Random Missing Input Data
Multi-domain data are widely leveraged in vision applications taking advantage of complementary information from different modalities, e. g., brain tumor segmentation from multi-parametric magnetic resonance imaging (MRI).
Cardiac Segmentation on Late Gadolinium Enhancement MRI: A Benchmark Study from Multi-Sequence Cardiac MR Segmentation Challenge
In addition, the paired MS-CMR images could enable algorithms to combine the complementary information from the other sequences for the segmentation of LGE CMR.
LAMP: Large Deep Nets with Automated Model Parallelism for Image Segmentation
In this work, we introduce Large deep 3D ConvNets with Automated Model Parallelism (LAMP) and investigate the impact of both input's and deep 3D ConvNets' size on segmentation accuracy.
TransMoMo: Invariance-Driven Unsupervised Video Motion Retargeting
We present a lightweight video motion retargeting approach TransMoMo that is capable of transferring motion of a person in a source video realistically to another video of a target person.
NeurReg: Neural Registration and Its Application to Image Segmentation
Furthermore, we design three segmentation frameworks based on the proposed registration framework: 1) atlas-based segmentation, 2) joint learning of both segmentation and registration tasks, and 3) multi-task learning with atlas-based segmentation as an intermediate feature.
Cardiac Segmentation of LGE MRI with Noisy Labels
In the first step, we register a small set of five LGE cardiac magnetic resonance (CMR) images with ground truth labels to a set of 40 target LGE CMR images without annotation.
Privacy-preserving Federated Brain Tumour Segmentation
Due to medical data privacy regulations, it is often infeasible to collect and share patient data in a centralised data lake.
Neural Multi-Scale Self-Supervised Registration for Echocardiogram Dense Tracking
In this work, we propose a neural multi-scale self-supervised registration (NMSR) method for automated myocardial and cardiac blood flow dense tracking.
Deep Learning for Automated Medical Image Analysis
Second, we will demonstrate how to use the weakly labeled data for the mammogram breast cancer diagnosis by efficiently design deep learning for multi-instance learning.
AnatomyNet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomy
Methods: Our deep learning model, called AnatomyNet, segments OARs from head and neck CT images in an end-to-end fashion, receiving whole-volume HaN CT images as input and generating masks of all OARs of interest in one shot.
DeepEM: Deep 3D ConvNets With EM For Weakly Supervised Pulmonary Nodule Detection
Recently deep learning has been witnessing widespread adoption in various medical image applications.
DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification
DeepLung consists of two components, nodule detection (identifying the locations of candidate nodules) and classification (classifying candidate nodules into benign or malignant).
Ranked #4 on
Lung Nodule Classification
on LIDC-IDRI
Adversarial Deep Structured Nets for Mass Segmentation from Mammograms
Mass segmentation provides effective morphological features which are important for mass diagnosis.
DeepLung: 3D Deep Convolutional Nets for Automated Pulmonary Nodule Detection and Classification
Considering the 3D nature of lung CT data, two 3D networks are designed for the nodule detection and classification respectively.
Automated Pulmonary Nodule Detection And Classification
Classification
+1
Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification
Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning (MIL) for labeling a set of instances/patches, we propose end-to-end trained deep multi-instance networks for mass classification based on whole mammogram without the aforementioned ROIs.
Leak Event Identification in Water Systems Using High Order CRF
Today, detection of anomalous events in civil infrastructures (e. g. water pipe breaks and leaks) is time consuming and often takes hours or days.
Adversarial Deep Structural Networks for Mammographic Mass Segmentation
Experimental results on two public datasets, INbreast and DDSM-BCRP, show that our end-to-end network combined with adversarial training achieves the-state-of-the-art results.
Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification
Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning for labeling a set of instances/patches, we propose end-to-end trained deep multi-instance networks for mass classification based on whole mammogram without the aforementioned costly need to annotate the training data.
Co-occurrence Feature Learning for Skeleton based Action Recognition using Regularized Deep LSTM Networks
Skeleton based action recognition distinguishes human actions using the trajectories of skeleton joints, which provide a very good representation for describing actions.
Deep Trans-layer Unsupervised Networks for Representation Learning
Compared to traditional deep learning methods, the implemented feature learning method has much less parameters and is validated in several typical experiments, such as digit recognition on MNIST and MNIST variations, object recognition on Caltech 101 dataset and face verification on LFW dataset.
Constrained Extreme Learning Machines: A Study on Classification Cases
Extreme learning machine (ELM) is an extremely fast learning method and has a powerful performance for pattern recognition tasks proven by enormous researches and engineers.
