Search Results for author:
Found 153 papers, 83 papers with code
Self-Supervised CycleGAN for Object-Preserving Image-to-Image Domain Adaptation
Recent generative adversarial network (GAN) based methods (e. g., CycleGAN) are prone to fail at preserving image-objects in image-to-image translation, which reduces their practicality on tasks such as domain adaptation.
Dual Adversarial Network for Deep Active Learning
In this paper, we investigate the overlapping problem of recent uncertainty-based approaches and propose to alleviate the issue by taking representativeness into consideration.
DeltaNet: Conditional Medical Report Generation for COVID-19 Diagnosis
To reduce the workload of radiologists, we propose DeltaNet to generate medical reports automatically.
Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Network
Accurately predicting drug-drug interactions (DDI) for emerging drugs, which offer possibilities for treating and alleviating diseases, with computational methods can improve patient care and contribute to efficient drug development.
MOELoRA: An MOE-based Parameter Efficient Fine-Tuning Method for Multi-task Medical Applications
Additionally, we propose a task-motivated gate function for all MOELoRA layers that can regulate the contributions of each expert and generate distinct parameters for various tasks.
Relation-aware Ensemble Learning for Knowledge Graph Embedding
Knowledge graph (KG) embedding is a fundamental task in natural language processing, and various methods have been proposed to explore semantic patterns in distinctive ways.
Ranked #1 on
Link Property Prediction
on ogbl-biokg
UniHead: Unifying Multi-Perception for Detection Heads
More precisely, our approach (1) introduces deformation perception, enabling the model to adaptively sample object features; (2) proposes a Dual-axial Aggregation Transformer (DAT) to adeptly model long-range dependencies, thereby achieving global perception; and (3) devises a Cross-task Interaction Transformer (CIT) that facilitates interaction between the classification and localization branches, thus aligning the two tasks.
Automatic view plane prescription for cardiac magnetic resonance imaging via supervision by spatial relationship between views
Then, a multi-view planning strategy is proposed to aggregate information from the predicted heatmaps for all the source views of a target plane, for a globally optimal prescription, mimicking the similar strategy practiced by skilled human prescribers.
JoTR: A Joint Transformer and Reinforcement Learning Framework for Dialog Policy Learning
In addition, JoTR employs reinforcement learning with a reward-shaping mechanism to efficiently finetune the word-level dialogue policy, which allows the model to learn from its interactions, improving its performance over time.
DS-Depth: Dynamic and Static Depth Estimation via a Fusion Cost Volume
Nevertheless, the dynamic cost volume inevitably generates extra occlusions and noise, thus we alleviate this by designing a fusion module that makes static and dynamic cost volumes compensate for each other.
Gait Cycle-Inspired Learning Strategy for Continuous Prediction of Knee Joint Trajectory from sEMG
This paper proposes a model integrating two gait cycle-inspired learning strategies to mitigate the challenge for predicting human knee joint trajectory.
BoxDiff: Text-to-Image Synthesis with Training-Free Box-Constrained Diffusion
As such paired data is time-consuming and labor-intensive to acquire and restricted to a closed set, this potentially becomes the bottleneck for applications in an open world.
You've Got Two Teachers: Co-evolutionary Image and Report Distillation for Semi-supervised Anatomical Abnormality Detection in Chest X-ray
Inversely, we also use the prediction of the vision detection model for abnormality-guided pseudo classification label refinement (APCLR) in the auxiliary report classification task, and propose a co-evolution strategy where the vision and report models mutually promote each other with RPDLR and APCLR performed alternatively.
CoAD: Automatic Diagnosis through Symptom and Disease Collaborative Generation
Automatic diagnosis (AD), a critical application of AI in healthcare, employs machine learning techniques to assist doctors in gathering patient symptom information for precise disease diagnosis.
K-Space-Aware Cross-Modality Score for Synthesized Neuroimage Quality Assessment
To further reflect the frequency-specific information from the magnetic resonance imaging principles, both k-space features and vision features are obtained and employed in our comprehensive encoders with a frequency reconstruction penalty.
Semi-supervised Credit Card Fraud Detection via Attribute-driven Graph Representation
Then we pass messages among the nodes through a Gated Temporal Attention Network (GTAN) to learn the transaction representation.
Ranked #1 on
Node Classification
on Amazon-Fraud
MEPNet: A Model-Driven Equivariant Proximal Network for Joint Sparse-View Reconstruction and Metal Artifact Reduction in CT Images
Sparse-view computed tomography (CT) has been adopted as an important technique for speeding up data acquisition and decreasing radiation dose.
Dynamically Masked Discriminator for Generative Adversarial Networks
By treating the generated data in training as a stream, we propose to detect whether the discriminator slows down the learning of new knowledge in generated data.
Cross-Modal Vertical Federated Learning for MRI Reconstruction
To this end, we formulate this practical-yet-challenging cross-modal vertical federated learning task, in which shape data from multiple hospitals have different modalities with a small amount of multi-modality data collected from the same individuals.
CSS: A Large-scale Cross-schema Chinese Text-to-SQL Medical Dataset
Furthermore, we present CSS, a large-scale CrosS-Schema Chinese text-to-SQL dataset, to carry on corresponding studies.
VisorGPT: Learning Visual Prior via Generative Pre-Training
Experimental results demonstrate that VisorGPT can effectively model the visual prior, which can be employed for many vision tasks, such as customizing accurate human pose for conditional image synthesis models like ControlNet.
Qualifying Chinese Medical Licensing Examination with Knowledge Enhanced Generative Pre-training Model
Experimental results show that directly applying ChatGPT fails to qualify the CNMLE at a score of 51 (i. e., only 51\% of questions are answered correctly).
M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing Modalities
In the first stage, a multimodal masked autoencoder (M3AE) is proposed, where both random modalities (i. e., modality dropout) and random patches of the remaining modalities are masked for a reconstruction task, for self-supervised learning of robust multimodal representations against missing modalities.
Improving GAN Training via Feature Space Shrinkage
The evaluation results demonstrate that our AdaptiveMix can facilitate the training of GANs and effectively improve the image quality of generated samples.
RECIST Weakly Supervised Lesion Segmentation via Label-Space Co-Training
As an essential indicator for cancer progression and treatment response, tumor size is often measured following the response evaluation criteria in solid tumors (RECIST) guideline in CT slices.
Interactive Segmentation as Gaussian Process Classification
Against this issue, in this paper, we propose to formulate the IS task as a Gaussian process (GP)-based pixel-wise binary classification model on each image.
Exploring Social Media for Early Detection of Depression in COVID-19 Patients
Secondly, We conducted an extensive analysis of this dataset to investigate the characteristic of COVID-19 patients with a higher risk of depression.
Deep-Learning Tool for Early Identifying Non-Traumatic Intracranial Hemorrhage Etiology based on CT Scan
Conclusions: The proposed deep learning algorithms can be an effective tool for early identification of hemorrhage etiologies based on NCCT scans.
MADAv2: Advanced Multi-Anchor Based Active Domain Adaptation Segmentation
Unsupervised domain adaption has been widely adopted in tasks with scarce annotated data.
FemtoDet: An Object Detection Baseline for Energy Versus Performance Tradeoffs
Efficient detectors for edge devices are often optimized for parameters or speed count metrics, which remain in weak correlation with the energy of detectors.
Ranked #29 on
Object Detection
on PASCAL VOC 2007
A New Perspective to Boost Vision Transformer for Medical Image Classification
The experimental results validate the superiority of our BOLT for medical image classification, compared to ImageNet pretrained weights and state-of-the-art self-supervised learning approaches.
SemiCVT: Semi-Supervised Convolutional Vision Transformer for Semantic Segmentation
Semi-supervised learning improves data efficiency of deep models by leveraging unlabeled samples to alleviate the reliance on a large set of labeled samples.
AdaptiveMix: Improving GAN Training via Feature Space Shrinkage
The evaluation results demonstrate that our AdaptiveMix can facilitate the training of GANs and effectively improve the image quality of generated samples.
Interactive Segmentation As Gaussion Process Classification
Against this issue, in this paper, we propose to formulate the IS task as a Gaussian process (GP)-based pixel-wise binary classification model on each image.
Orientation-Shared Convolution Representation for CT Metal Artifact Learning
During X-ray computed tomography (CT) scanning, metallic implants carrying with patients often lead to adverse artifacts in the captured CT images and then impair the clinical treatment.
Lesion Guided Explainable Few Weak-shot Medical Report Generation
To this end, we propose a lesion guided explainable few weak-shot medical report generation framework that learns correlation between seen and novel classes through visual and semantic feature alignment, aiming to generate medical reports for diseases not observed in training.
DeltaNet:Conditional Medical Report Generation for COVID-19 Diagnosis
To reduce the workload of radiologists, we propose DeltaNet to generate medical reports automatically.
Knowing the Past to Predict the Future: Reinforcement Virtual Learning
Reinforcement Learning (RL)-based control system has received considerable attention in recent decades.
Decoupled Mixup for Generalized Visual Recognition
Since the observation is that noise-prone regions such as textural and clutter backgrounds are adverse to the generalization ability of CNN models during training, we enhance features from discriminative regions and suppress noise-prone ones when combining an image pair.
MCSCSet: A Specialist-annotated Dataset for Medical-domain Chinese Spelling Correction
In this work, we define the task of Medical-domain Chinese Spelling Correction and propose MCSCSet, a large scale specialist-annotated dataset that contains about 200k samples.
Optical Character Recognition
Optical Character Recognition (OCR)
+1
Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning
Providing Emotional Support (ES) to soothe people in emotional distress is an essential capability in social interactions.
Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words
Prompt-based fine-tuning for pre-trained models has proven effective for many natural language processing tasks under few-shot settings in general domain.
A Benchmark for Weakly Semi-Supervised Abnormality Localization in Chest X-Rays
Such a point annotation setting can provide weakly instance-level information for abnormality localization with a marginal annotation cost.
Multi-modal Contrastive Representation Learning for Entity Alignment
Multi-modal entity alignment aims to identify equivalent entities between two different multi-modal knowledge graphs, which consist of structural triples and images associated with entities.
Ranked #2 on
Multi-modal Entity Alignment
on UMVM-oea-d-w-v1
(using extra training data)
Seg4Reg+: Consistency Learning between Spine Segmentation and Cobb Angle Regression
In this study, we propose a novel multi-task framework, named Seg4Reg+, which jointly optimizes the segmentation and regression networks.
Combating Mode Collapse in GANs via Manifold Entropy Estimation
In this paper, we propose a novel training pipeline to address the mode collapse issue of GANs.
Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation
A key to this challenging task is to fully utilize the information in the support images by exploiting fine-grained correlations between the query and support images.
Ranked #4 on
Few-Shot Semantic Segmentation
on COCO-20i (1-shot)
Deformer: Towards Displacement Field Learning for Unsupervised Medical Image Registration
In this study, we propose a novel Deformer module along with a multi-scale framework for the deformable image registration task.
mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation
Concretely, we propose a novel multimodal Medical Transformer (mmFormer) for incomplete multimodal learning with three main components: the hybrid modality-specific encoders that bridge a convolutional encoder and an intra-modal Transformer for both local and global context modeling within each modality; an inter-modal Transformer to build and align the long-range correlations across modalities for modality-invariant features with global semantics corresponding to tumor region; a decoder that performs a progressive up-sampling and fusion with the modality-invariant features to generate robust segmentation.
Ranked #61 on
Semantic Segmentation
on NYU Depth v2
Adaptive Convolutional Dictionary Network for CT Metal Artifact Reduction
By unfolding every iterative substep of the proposed algorithm into a network module, we explicitly embed the prior structure into a deep network, \emph{i. e.,} a clear interpretability for the MAR task.
Robust Representation via Dynamic Feature Aggregation
With the regularization and orthogonal classifier, a more compact embedding space can be obtained, which accordingly improves the model robustness against adversarial attacks.
"My nose is running.""Are you also coughing?": Building A Medical Diagnosis Agent with Interpretable Inquiry Logics
In this paper, we explore how to bring interpretability to data-driven DSMD.
Learning Shape Priors by Pairwise Comparison for Robust Semantic Segmentation
A cosine similarity based attention module is proposed to fuse the information from both encoders, to utilize both types of prior information encoded by the template-encoder and model the inter-subject similarity for each foreground class.
Domain Adaptation Meets Zero-Shot Learning: An Annotation-Efficient Approach to Multi-Modality Medical Image Segmentation
Second, we propose a relation prototype awareness module to make the zero-shot model aware of information contained in the prototypes.
DFTR: Depth-supervised Fusion Transformer for Salient Object Detection
This paper, for the first time, seeks to expand the applicability of depth supervision to the Transformer architecture.
Deep Convolutional Neural Networks for Molecular Subtyping of Gliomas Using Magnetic Resonance Imaging
Methods: A DCNN model was developed for the prediction of the five glioma subtypes based on a hierarchical classification paradigm.
Conquering Data Variations in Resolution: A Slice-Aware Multi-Branch Decoder Network
Based on the aforementioned innovations, we achieve state-of-the-art results on the MICCAI 2017 Liver Tumor Segmentation (LiTS) dataset.
Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retina OCT Images
To the best of our knowledge, this is the first study that attempts 3D retinal layer segmentation in volumetric OCT images based on CNNs.
Nuclei Segmentation with Point Annotations from Pathology Images via Self-Supervised Learning and Co-Training
Third, a self-supervised visual representation learning method is tailored for nuclei segmentation of pathology images that transforms the hematoxylin component images into the H&E stained images to gain better understanding of the relationship between the nuclei and cytoplasm.
Cross-Modality Neuroimage Synthesis: A Survey
This is followed by a stepwise in-depth analysis to evaluate how cross-modality neuroimage synthesis improves the performance of its downstream tasks.
FedMed-ATL: Misaligned Unpaired Brain Image Synthesis via Affine Transform Loss
The proposed method demonstrates the advanced performance in both the quality of our synthesized results under a severely misaligned and unpaired data setting, and better stability than other GAN-based algorithms.
FedMed-GAN: Federated Domain Translation on Unsupervised Cross-Modality Brain Image Synthesis
There is a clear need to launch a federated learning and facilitate the integration of the dispersed data from different institutions.
Relational Experience Replay: Continual Learning by Adaptively Tuning Task-wise Relationship
Continual learning is a promising machine learning paradigm to learn new tasks while retaining previously learned knowledge over streaming training data.
InDuDoNet+: A Deep Unfolding Dual Domain Network for Metal Artifact Reduction in CT Images
To alleviate these issues, in the paper, we construct a novel deep unfolding dual domain network, termed InDuDoNet+, into which CT imaging process is finely embedded.
A Unified Framework for Generalized Low-Shot Medical Image Segmentation with Scarce Data
In this work, we propose a unified framework for generalized low-shot (one- and few-shot) medical image segmentation based on distance metric learning (DML).
Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation
In semi-supervised medical image segmentation, most previous works draw on the common assumption that higher entropy means higher uncertainty.
Alleviating Noisy-label Effects in Image Classification via Probability Transition Matrix
The hard samples, which are beneficial for classifier learning, are often mistakenly treated as noises in such a setting since both the hard samples and ones with noisy labels lead to a relatively larger loss value than the easy cases.
Tell Me How to Survey: Literature Review Made Simple with Automatic Reading Path Generation
In this paper, we introduce a new task named Reading Path Generation (RPG) which aims at automatically producing a path of papers to read for a given query.
Unsupervised Representation Learning Meets Pseudo-Label Supervised Self-Distillation: A New Approach to Rare Disease Classification
Second, we integrate the URL with pseudo-label supervised classification for effective self-distillation of the knowledge about the rare diseases, composing a hybrid approach taking advantages of both unsupervised and (pseudo-) supervised learning on the base dataset.
PAC-Bayes Information Bottleneck
Then, we build an IIW-based information bottleneck on the trade-off between accuracy and information complexity of NNs, namely PIB.
All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation
Observing this, we ask an unexplored but interesting question: can we exploit the unlabeled data via explicit real label supervision for semi-supervised training?
Training Automatic View Planner for Cardiac MR Imaging via Self-Supervision by Spatial Relationship between Views
Then, a multi-view planning strategy is proposed to aggregate information from the predicted heatmaps for all the source views of a target view, for a globally optimal prescription.
InDuDoNet: An Interpretable Dual Domain Network for CT Metal Artifact Reduction
For the task of metal artifact reduction (MAR), although deep learning (DL)-based methods have achieved promising performances, most of them suffer from two problems: 1) the CT imaging geometry constraint is not fully embedded into the network during training, leaving room for further performance improvement; 2) the model interpretability is lack of sufficient consideration.
Refining BERT Embeddings for Document Hashing via Mutual Information Maximization
As a first try, we modify existing generative hashing models to accommodate the BERT embeddings.
A New Bidirectional Unsupervised Domain Adaptation Segmentation Framework
Domain shift happens in cross-domain scenarios commonly because of the wide gaps between different domains: when applying a deep learning model well-trained in one domain to another target domain, the model usually performs poorly.
Multi-Anchor Active Domain Adaptation for Semantic Segmentation
Unsupervised domain adaption has proven to be an effective approach for alleviating the intensive workload of manual annotation by aligning the synthetic source-domain data and the real-world target-domain samples.
CONNER: A Cascade Count and Measurement Extraction Tool for Scientific Discourse
Accordingly, we propose CONNER, a cascade count and measurement extraction tool that can identify entities and the corresponding relations in a two-step pipeline model.
RECIST-Net: Lesion detection via grouping keypoints on RECIST-based annotation
Universal lesion detection in computed tomography (CT) images is an important yet challenging task due to the large variations in lesion type, size, shape, and appearance.
RCDNet: An Interpretable Rain Convolutional Dictionary Network for Single Image Deraining
To handle such an ill-posed single image deraining task, in this paper, we specifically build a novel deep architecture, called rain convolutional dictionary network (RCDNet), which embeds the intrinsic priors of rain streaks and has clear interpretability.
Double-Uncertainty Guided Spatial and Temporal Consistency Regularization Weighting for Learning-based Abdominal Registration
Such convention has two limitations: (i) Besides the laborious grid search for the optimal fixed weight, the regularization strength of a specific image pair should be associated with the content of the images, thus the "one value fits all" training scheme is not ideal; (ii) Only spatially regularizing the transformation may neglect some informative clues related to the ill-posedness.
Mutual-GAN: Towards Unsupervised Cross-Weather Adaptation with Mutual Information Constraint
In practical applications, the outdoor weather and illumination are changeable, e. g., cloudy and nighttime, which results in a significant drop of semantic segmentation accuracy of CNN only trained with daytime data.
Residual Moment Loss for Medical Image Segmentation
Location information is proven to benefit the deep learning models on capturing the manifold structure of target objects, and accordingly boosts the accuracy of medical image segmentation.
Calibrated RGB-D Salient Object Detection
Complex backgrounds and similar appearances between objects and their surroundings are generally recognized as challenging scenarios in Salient Object Detection (SOD).
Ranked #13 on
Thermal Image Segmentation
on RGB-T-Glass-Segmentation
Learning Calibrated Medical Image Segmentation via Multi-Rater Agreement Modeling
To our knowledge, our work is the first in producing calibrated predictions under different expertise levels for medical image segmentation.
PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction
Joint extraction of entities and relations from unstructured texts is a crucial task in information extraction.
Prototypical Graph Contrastive Learning
However, since for a query, its negatives are uniformly sampled from all graphs, existing methods suffer from the critical sampling bias issue, i. e., the negatives likely having the same semantic structure with the query, leading to performance degradation.
PRASEMap: A Probabilistic Reasoning and Semantic Embedding based Knowledge Graph Alignment System
Knowledge Graph (KG) alignment aims at finding equivalent entities and relations (i. e., mappings) between two KGs.
LENAS: Learning-based Neural Architecture Search and Ensemble for 3D Radiotherapy Dose Prediction
To the best of our knowledge, this is the first attempt to investigate NAS and knowledge distillation in ensemble learning, especially in the field of medical image analysis.
The Medical Segmentation Decathlon
Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem.
Noisy Labels are Treasure: Mean-Teacher-Assisted Confident Learning for Hepatic Vessel Segmentation
Manually segmenting the hepatic vessels from Computer Tomography (CT) is far more expertise-demanding and laborious than other structures due to the low-contrast and complex morphology of vessels, resulting in the extreme lack of high-quality labeled data.
Integrating Semantics and Neighborhood Information with Graph-Driven Generative Models for Document Retrieval
With the need of fast retrieval speed and small memory footprint, document hashing has been playing a crucial role in large-scale information retrieval.
Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting
This paper explores the task of Difficulty-Controllable Question Generation (DCQG), which aims at generating questions with required difficulty levels.
OntoEA: Ontology-guided Entity Alignment via Joint Knowledge Graph Embedding
Semantic embedding has been widely investigated for aligning knowledge graph (KG) entities.
Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding
Knowledge Graph (KG) alignment is to discover the mappings (i. e., equivalent entities, relations, and others) between two KGs.
Imperfect also Deserves Reward: Multi-Level and Sequential Reward Modeling for Better Dialog Management
For task-oriented dialog systems, training a Reinforcement Learning (RL) based Dialog Management module suffers from low sample efficiency and slow convergence speed due to the sparse rewards in RL. To solve this problem, many strategies have been proposed to give proper rewards when training RL, but their rewards lack interpretability and cannot accurately estimate the distribution of state-action pairs in real dialogs.
Generalized Organ Segmentation by Imitating One-shot Reasoning using Anatomical Correlation
In this paper, we show that such process can be integrated into the one-shot segmentation task which is a very challenging but meaningful topic.
Stabilized Medical Image Attacks
However, a threat to these systems arises that adversarial attacks make CNNs vulnerable.
Lifelong Learning based Disease Diagnosis on Clinical Notes
Current deep learning based disease diagnosis systems usually fall short in catastrophic forgetting, i. e., directly fine-tuning the disease diagnosis model on new tasks usually leads to abrupt decay of performance on previous tasks.
MixSearch: Searching for Domain Generalized Medical Image Segmentation Architectures
Considering the scarcity of medical data, most datasets in medical image analysis are an order of magnitude smaller than those of natural images.
Enquire One's Parent and Child Before Decision: Fully Exploit Hierarchical Structure for Self-Supervised Taxonomy Expansion
Taxonomy is a hierarchically structured knowledge graph that plays a crucial role in machine intelligence.
Deep Symmetric Adaptation Network for Cross-modality Medical Image Segmentation
These typical methods usually utilize a translation network to transform images from the source domain to target domain or train the pixel-level classifier merely using translated source images and original target images.
Stabilized Medical Attacks
We further analyze the KL-divergence of the proposed loss function and find that the loss stabilization term makes the perturbations updated towards a fixed objective spot while deviating from the ground truth.
Ensembled ResUnet for Anatomical Brain Barriers Segmentation
However, due to the visual and anatomical differences between different modalities, the accurate segmentation of brain structures becomes challenging.
A Hierarchical Feature Constraint to Camouflage Medical Adversarial Attacks
Deep neural networks (DNNs) for medical images are extremely vulnerable to adversarial examples (AEs), which poses security concerns on clinical decision making.
Poisoning Semi-supervised Federated Learning via Unlabeled Data: Attacks and Defenses
Extensive case studies have shown that our attacks are effective on different datasets and common semi-supervised learning methods.
An Industry Evaluation of Embedding-based Entity Alignment
Embedding-based entity alignment has been widely investigated in recent years, but most proposed methods still rely on an ideal supervised learning setting with a large number of unbiased seed mappings for training and validation, which significantly limits their usage.
MedDG: An Entity-Centric Medical Consultation Dataset for Entity-Aware Medical Dialogue Generation
To push forward the future research on building expert-sensitive medical dialogue system, we proposes two kinds of medical dialogue tasks based on MedDG dataset.
Finding Influential Instances for Distantly Supervised Relation Extraction
Distant supervision (DS) is a strong way to expand the datasets for enhancing relation extraction (RE) models but often suffers from high label noise.
Online Disease Self-diagnosis with Inductive Heterogeneous Graph Convolutional Networks
We propose a Healthcare Graph Convolutional Network (HealGCN) to offer disease self-diagnosis service for online users based on Electronic Healthcare Records (EHRs).
Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback
Counterfactual learning for dealing with missing-not-at-random data (MNAR) is an intriguing topic in the recommendation literature since MNAR data are ubiquitous in modern recommender systems.
From Rain Generation to Rain Removal
For the single image rain removal (SIRR) task, the performance of deep learning (DL)-based methods is mainly affected by the designed deraining models and training datasets.
TR-GAN: Topology Ranking GAN with Triplet Loss for Retinal Artery/Vein Classification
A topology ranking discriminator based on ordinal regression is proposed to rank the topological connectivity level of the ground-truth, the generated A/V mask and the intentionally shuffled mask.
Difficulty-aware Glaucoma Classification with Multi-Rater Consensus Modeling
However, when being used for model training, only the final ground-truth label is utilized, while the critical information contained in the raw multi-rater gradings regarding the image being an easy/hard case is discarded.
Learning Crisp Edge Detector Using Logical Refinement Network
Edge detection is a fundamental problem in different computer vision tasks.
MI^2GAN: Generative Adversarial Network for Medical Image Domain Adaptation using Mutual Information Constraint
Domain shift between medical images from multicentres is still an open question for the community, which degrades the generalization performance of deep learning models.
Instance-aware Self-supervised Learning for Nuclei Segmentation
Due to the wide existence and large morphological variances of nuclei, accurate nuclei instance segmentation is still one of the most challenging tasks in computational pathology.
Leveraging Undiagnosed Data for Glaucoma Classification with Teacher-Student Learning
Recently, deep learning has been adopted to the glaucoma classification task with performance comparable to that of human experts.
GREEN: a Graph REsidual rE-ranking Network for Grading Diabetic Retinopathy
In this paper, we propose a Graph REsidual rE-ranking Network (GREEN) to introduce a class dependency prior into the original image classification network.
Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical Image Segmentation
A novel pseudo-label (namely self-loop uncertainty), generated by recurrently optimizing the neural network with a self-supervised task, is adopted as the ground-truth for the unlabeled images to augment the training set and boost the segmentation accuracy.
Distractor-Aware Neuron Intrinsic Learning for Generic 2D Medical Image Classifications
We formulate a novel distractor-aware loss that encourages large distance between the original image and its distractor in the feature space.
A Macro-Micro Weakly-supervised Framework for AS-OCT Tissue Segmentation
Primary angle closure glaucoma (PACG) is the leading cause of irreversible blindness among Asian people.
Deep Image Clustering with Category-Style Representation
In this paper, we propose a novel deep image clustering framework to learn a category-style latent representation in which the category information is disentangled from image style and can be directly used as the cluster assignment.
Multi-Task Neural Networks with Spatial Activation for Retinal Vessel Segmentation and Artery/Vein Classification
In this paper, we propose a multi-task deep neural network with spatial activation mechanism that is able to segment full retinal vessel, artery and vein simultaneously, without the pre-requirement of vessel segmentation.
Superpixel-Guided Label Softening for Medical Image Segmentation
If a superpixel intersects with the annotation boundary, we consider a high probability of uncertain labeling within this area.
Revisiting Rubik's Cube: Self-supervised Learning with Volume-wise Transformation for 3D Medical Image Segmentation
In this paper, we propose a novel self-supervised learning framework for volumetric medical images.
Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs By Comparing Image Representations
In deep learning era, pretrained models play an important role in medical image analysis, in which ImageNet pretraining has been widely adopted as the best way.
Learning and Exploiting Interclass Visual Correlations for Medical Image Classification
In this paper, we present the Class-Correlation Learning Network (CCL-Net) to learn interclass visual correlations from given training data, and produce soft labels to help with classification tasks.
Cross-denoising Network against Corrupted Labels in Medical Image Segmentation with Domain Shift
However, \textit{domain shift} and \textit{corrupted annotations}, which are two common problems in medical imaging, dramatically degrade the performance of DCNNs in practice.
Multi-Modality Generative Adversarial Networks with Tumor Consistency Loss for Brain MR Image Synthesis
We use the synthesized modalities by TC-MGAN to boost the tumor segmentation accuracy, and the results demonstrate its effectiveness.
A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging
Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) widely used for visualizing diseased cardiac structures, is a crucial first step for clinical diagnosis and treatment.
Generative Adversarial Networks for Video-to-Video Domain Adaptation
Two colonoscopic datasets from different centres, i. e., CVC-Clinic and ETIS-Larib, are adopted to evaluate the performance of domain adaptation of our VideoGAN.
Crossover-Net: Leveraging the Vertical-Horizontal Crossover Relation for Robust Segmentation
Robust segmentation for non-elongated tissues in medical images is hard to realize due to the large variation of the shape, size, and appearance of these tissues in different patients.
Quality Control of Neuron Reconstruction Based on Deep Learning
To ensure the quality of reconstructed neurons and provide guidance for annotators to improve their efficiency, we propose a deep learning based quality control method for neuron reconstruction in this paper.
LT-Net: Label Transfer by Learning Reversible Voxel-wise Correspondence for One-shot Medical Image Segmentation
We introduce a one-shot segmentation method to alleviate the burden of manual annotation for medical images.
Identification of primary angle-closure on AS-OCT images with Convolutional Neural Networks
In this paper, we propose an efficient and accurate end-to-end architecture for angle-closure classification and scleral spur localization.
Self-supervised Feature Learning for 3D Medical Images by Playing a Rubik's Cube
Witnessed the development of deep learning, increasing number of studies try to build computer aided diagnosis systems for 3D volumetric medical data.
Uncertainty-Guided Domain Alignment for Layer Segmentation in OCT Images
Automatic and accurate segmentation for retinal and choroidal layers of Optical Coherence Tomography (OCT) is crucial for detection of various ocular diseases.
Semi-supervised Breast Lesion Detection in Ultrasound Video Based on Temporal Coherence
Breast lesion detection in ultrasound video is critical for computer-aided diagnosis.
Attentive CT Lesion Detection Using Deep Pyramid Inference with Multi-Scale Booster
Accurate lesion detection in computer tomography (CT) slices benefits pathologic organ analysis in the medical diagnosis process.
OctopusNet: A Deep Learning Segmentation Network for Multi-modal Medical Images
Two approaches are widely used in the literature to fuse multiple modalities in the segmentation networks: early-fusion (which stacks multiple modalities as different input channels) and late-fusion (which fuses the segmentation results from different modalities at the very end).
A GLCM Embedded CNN Strategy for Computer-aided Diagnosis in Intracerebral Hemorrhage
Computer-aided diagnosis (CADx) systems have been shown to assist radiologists by providing classifications of all kinds of medical images like Computed tomography (CT) and Magnetic resonance (MR).
X2CT-GAN: Reconstructing CT from Biplanar X-Rays with Generative Adversarial Networks
Computed tomography (CT) can provide a 3D view of the patient's internal organs, facilitating disease diagnosis, but it incurs more radiation dose to a patient and a CT scanner is much more cost prohibitive than an X-ray machine too.
Med3D: Transfer Learning for 3D Medical Image Analysis
The performance on deep learning is significantly affected by volume of training data.
TAN: Temporal Affine Network for Real-Time Left Ventricle Anatomical Structure Analysis Based on 2D Ultrasound Videos
Instead of using three networks with one dedicating to each task, we use a multi-task network to perform three tasks simultaneously.
Pyramid Network with Online Hard Example Mining for Accurate Left Atrium Segmentation
Accurately segmenting left atrium in MR volume can benefit the ablation procedure of atrial fibrillation.
When Semi-Supervised Learning Meets Transfer Learning: Training Strategies, Models and Datasets
While practitioners have had an intuitive understanding of these observations, we do a comprehensive emperical analysis and demonstrate that: (1) the gains from SSL techniques over a fully-supervised baseline are smaller when trained from a pre-trained model than when trained from random initialization, (2) when the domain of the source data used to train the pre-trained model differs significantly from the domain of the target task, the gains from SSL are significantly higher and (3) some SSL methods are able to advance fully-supervised baselines (like Pseudo-Label).
Face Completion with Semantic Knowledge and Collaborative Adversarial Learning
Unlike a conventional background inpainting approach that infers a missing area from image patches similar to the background, face completion requires semantic knowledge about the target object for realistic outputs.
