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Found 47 papers, 18 papers with code
Inferring from References with Differences for Semi-Supervised Node Classification on Graphs
Following the application of Deep Learning to graphic data, Graph Neural Networks (GNNs) have become the dominant method for Node Classification on graphs in recent years.
Ranked #1 on
Node Classification
on Amazon Photo
SIOD: Single Instance Annotated Per Category Per Image for Object Detection
Object detection under imperfect data receives great attention recently.
Expanding Low-Density Latent Regions for Open-Set Object Detection
Thus, unknown objects in low-density regions can be easily identified with the learned unknown probability.
Accurate and Generalizable Quantitative Scoring of Liver Steatosis from Ultrasound Images via Scalable Deep Learning
High diagnostic performance was observed across all viewpoints: area under the curves of the ROC to classify >=mild, >=moderate, =severe steatosis grades were 0. 85, 0. 90, and 0. 93, respectively.
Transformer-based Dual Relation Graph for Multi-label Image Recognition
Different from these researches, in this paper, we propose a novel Transformer-based Dual Relation learning framework, constructing complementary relationships by exploring two aspects of correlation, i. e., structural relation graph and semantic relation graph.
SAME: Deformable Image Registration based on Self-supervised Anatomical Embeddings
In this work, we introduce a fast and accurate method for unsupervised 3D medical image registration.
Heterogeneous Relational Complement for Vehicle Re-identification
The crucial problem in vehicle re-identification is to find the same vehicle identity when reviewing this object from cross-view cameras, which sets a higher demand for learning viewpoint-invariant representations.
Discriminator-Free Generative Adversarial Attack
The Deep Neural Networks are vulnerable toadversarial exam-ples(Figure 1), making the DNNs-based systems collapsed byadding the inconspicuous perturbations to the images.
Graph-Based High-Order Relation Discovery for Fine-Grained Recognition
Fine-grained object recognition aims to learn effective features that can identify the subtle differences between visually similar objects.
Ranked #15 on
Fine-Grained Image Classification
on CUB-200-2011
Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
Nowadays, Graph Neural Networks (GNNs) following the Message Passing paradigm become the dominant way to learn on graphic data.
Ranked #1 on
Node Classification
on Cora Full
Lesion Segmentation and RECIST Diameter Prediction via Click-driven Attention and Dual-path Connection
PDNet learns comprehensive and representative deep image features for our tasks and produces more accurate results on both lesion segmentation and RECIST diameter prediction.
Weakly-Supervised Universal Lesion Segmentation with Regional Level Set Loss
Accurately segmenting a variety of clinically significant lesions from whole body computed tomography (CT) scans is a critical task on precision oncology imaging, denoted as universal lesion segmentation (ULS).
Learning from Subjective Ratings Using Auto-Decoded Deep Latent Embeddings
Importantly, ADDLE does not expect multiple raters per image in training, meaning it can readily learn from data mined from hospital archives.
Sequential Learning on Liver Tumor Boundary Semantics and Prognostic Biomarker Mining
(2) The sampled deep vertex features with positional embedding are mapped into a sequential space and decoded by a multilayer perceptron (MLP) for semantic classification.
Memory-Associated Differential Learning
Conventional Supervised Learning approaches focus on the mapping from input features to output labels.
Ranked #1 on
Link Property Prediction
on ogbl-ddi
Deep Lesion Tracker: Monitoring Lesions in 4D Longitudinal Imaging Studies
In this work, we present deep lesion tracker (DLT), a deep learning approach that uses both appearance- and anatomical-based signals.
SAM: Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological Images
We introduce such an approach, called Self-supervised Anatomical eMbedding (SAM).
Learning from Multiple Datasets with Heterogeneous and Partial Labels for Universal Lesion Detection in CT
For example, DeepLesion is such a large-scale CT image dataset with lesions of various types, but it also has many unlabeled lesions (missing annotations).
Deep Volumetric Universal Lesion Detection using Light-Weight Pseudo 3D Convolution and Surface Point Regression
Identifying, measuring and reporting lesions accurately and comprehensively from patient CT scans are important yet time-consuming procedures for physicians.
Lymph Node Gross Tumor Volume Detection in Oncology Imaging via Relationship Learning Using Graph Neural Network
Specifically, we first utilize a 3D convolutional neural network with ROI-pooling to extract the GTV$_{LN}$'s instance-wise appearance features.
Lymph Node Gross Tumor Volume Detection and Segmentation via Distance-based Gating using 3D CT/PET Imaging in Radiotherapy
Finding, identifying and segmenting suspicious cancer metastasized lymph nodes from 3D multi-modality imaging is a clinical task of paramount importance.
CFAD: Coarse-to-Fine Action Detector for Spatiotemporal Action Localization
Most current pipelines for spatio-temporal action localization connect frame-wise or clip-wise detection results to generate action proposals, where only local information is exploited and the efficiency is hindered by dense per-frame localization.
Reliable Liver Fibrosis Assessment from Ultrasound using Global Hetero-Image Fusion and View-Specific Parameterization
Ultrasound (US) is a critical modality for diagnosing liver fibrosis.
One Click Lesion RECIST Measurement and Segmentation on CT Scans
Based on the results of the first network, the second one refines the lesion segmentation and RECIST estimation.
Harvesting, Detecting, and Characterizing Liver Lesions from Large-scale Multi-phase CT Data via Deep Dynamic Texture Learning
To this end, we propose a fully-automated and multi-stage liver tumor characterization framework designed for dynamic contrast computed tomography (CT).
Universal Lesion Detection by Learning from Multiple Heterogeneously Labeled Datasets
First, we learn a multi-head multi-task lesion detector using all datasets and generate lesion proposals on DeepLesion.
Ranked #3 on
Medical Object Detection
on DeepLesion
(using extra training data)
Detecting Scatteredly-Distributed, Small, andCritically Important Objects in 3D OncologyImaging via Decision Stratification
We focus on the detection and segmentation of oncology-significant (or suspicious cancer metastasized) lymph nodes (OSLNs), which has not been studied before as a computational task.
Lesion Harvester: Iteratively Mining Unlabeled Lesions and Hard-Negative Examples at Scale
This is the goal of our work, where we develop a powerful system to harvest missing lesions from the DeepLesion dataset at high precision.
MULAN: Multitask Universal Lesion Analysis Network for Joint Lesion Detection, Tagging, and Segmentation
When reading medical images such as a computed tomography (CT) scan, radiologists generally search across the image to find lesions, characterize and measure them, and then describe them in the radiological report.
Ranked #5 on
Medical Object Detection
on DeepLesion
A self-attention based deep learning method for lesion attribute detection from CT reports
In radiology, radiologists not only detect lesions from the medical image, but also describe them with various attributes such as their type, location, size, shape, and intensity.
Holistic and Comprehensive Annotation of Clinically Significant Findings on Diverse CT Images: Learning from Radiology Reports and Label Ontology
In radiologists' routine work, one major task is to read a medical image, e. g., a CT scan, find significant lesions, and describe them in the radiology report.
Fine-grained lesion annotation in CT images with knowledge mined from radiology reports
To address this problem, we define a set of 145 labels based on RadLex to describe a large variety of lesions in the DeepLesion dataset.
ULDor: A Universal Lesion Detector for CT Scans with Pseudo Masks and Hard Negative Example Mining
To address this problem, this work constructs a pseudo mask for each lesion region that can be considered as a surrogate of the real mask, based on which the Mask R-CNN is employed for lesion detection.
Attention Driven Person Re-identification
Person re-identification (ReID) is a challenging task due to arbitrary human pose variations, background clutters, etc.
Deep Learning for Image Denoising: A Survey
Since the proposal of big data analysis and Graphic Processing Unit (GPU), the deep learning technology has received a great deal of attention and has been widely applied in the field of imaging processing.
CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement
The first GAN reduces the noise in the CT image and the second GAN generates a higher resolution image with enhanced boundaries and high contrast.
Accurate Weakly-Supervised Deep Lesion Segmentation using Large-Scale Clinical Annotations: Slice-Propagated 3D Mask Generation from 2D RECIST
Volumetric lesion segmentation from computed tomography (CT) images is a powerful means to precisely assess multiple time-point lesion/tumor changes.
3D Context Enhanced Region-based Convolutional Neural Network for End-to-End Lesion Detection
3D context is known to be helpful in this differentiation task.
Ranked #8 on
Medical Object Detection
on DeepLesion
Accurate Weakly Supervised Deep Lesion Segmentation on CT Scans: Self-Paced 3D Mask Generation from RECIST
Toward this end, we introduce a convolutional neural network based weakly supervised self-paced segmentation (WSSS) method to 1) generate the initial lesion segmentation on the axial RECIST-slice; 2) learn the data distribution on RECIST-slices; 3) adapt to segment the whole volume slice by slice to finally obtain a volumetric segmentation.
Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database
Then, a triplet network is utilized to learn lesion embeddings with a sequential sampling strategy to depict their hierarchical similarity structure.
DeepLesion: Automated Deep Mining, Categorization and Detection of Significant Radiology Image Findings using Large-Scale Clinical Lesion Annotations
We categorize the collection of lesions using an unsupervised deep mining scheme to generate clustered pseudo lesion labels.
Exploiting Multi-Grain Ranking Constraints for Precisely Searching Visually-Similar Vehicles
In this paper, we model the relationship of vehicle images as multiple grains.
Information Design in Crowdfunding under Thresholding Policies
We introduce a heuristic algorithm to dynamically compute information-disclosure policies for the entrepreneur, followed by an empirical evaluation to demonstrate its competitiveness over the widely-adopted immediate-disclosure policy.
Learning a Repression Network for Precise Vehicle Search
The growing explosion in the use of surveillance cameras in public security highlights the importance of vehicle search from large-scale image databases.
Unsupervised Body Part Regression via Spatially Self-ordering Convolutional Neural Networks
In this paper, we propose a convolutional neural network (CNN) based Unsupervised Body part Regression (UBR) algorithm to address this problem.
Local Multiple Directional Pattern of Palmprint Image
Conventional methods usually capture the only one of the most dominant direction of palmprint images.
Learning Domain-Invariant Subspace using Domain Features and Independence Maximization
In this paper, we focus on the problem of instrumental variation and time-varying drift in the field of sensors and measurement, which can be viewed as discrete and continuous distributional change in the feature space.
