Search Results for author:
Found 190 papers, 109 papers with code
DeltaNet: Conditional Medical Report Generation for COVID-19 Diagnosis
To reduce the workload of radiologists, we propose DeltaNet to generate medical reports automatically.
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.
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.
Mitigating Hallucinations of Large Language Models in Medical Information Extraction via Contrastive Decoding
In this paper, we address these hallucination issues in the context of Medical Information Extraction (MIE) tasks by introducing ALternate Contrastive Decoding (ALCD).
PASS:Test-Time Prompting to Adapt Styles and Semantic Shapes in Medical Image Segmentation
Test-time adaptation (TTA) has emerged as a promising paradigm to handle the domain shifts at test time for medical images from different institutions without using extra training data.
Large Language Model Empowered Embedding Generator for Sequential Recommendation
RAT refines the embeddings to be optimally suited for SRS.
Learning Spectral-Decomposed Tokens for Domain Generalized Semantic Segmentation
In this paper, we present a novel Spectral-dEcomposed Token (SET) learning framework to advance the frontier.
A Refreshed Similarity-based Upsampler for Direct High-Ratio Feature Upsampling
These shortcomings make the existing methods along this pipeline primarily applicable to hierarchical network architectures with iterative features as guidance and they are not readily extended to a broader range of structures, especially for a direct high-ratio upsampling.
AutoPal: Autonomous Adaptation to Users for Personal AI Companionship
However, imbuing agents with autonomous adaptability presents unique challenges, including identifying optimal adaptations to meet users' expectations and ensuring a smooth transition during the adaptation process.
MoME: Mixture of Multimodal Experts for Cancer Survival Prediction
To address these issues, we propose a Biased Progressive Encoding (BPE) paradigm, performing encoding and fusion simultaneously.
Prototype Correlation Matching and Class-Relation Reasoning for Few-Shot Medical Image Segmentation
Specifically, in order to address false pixel correlation match brought by large intra-class variations, we propose a prototype correlation matching module to mine representative prototypes that can characterize diverse visual information of different appearances well.
LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendation
As one of the pioneers in this field, we propose the Large Language Models Enhancement framework for Sequential Recommendation (LLM-ESR).
Wearable-based behaviour interpolation for semi-supervised human activity recognition
While traditional feature engineering for Human Activity Recognition (HAR) involves a trial-anderror process, deep learning has emerged as a preferred method for high-level representations of sensor-based human activities.
AnomalyXFusion: Multi-modal Anomaly Synthesis with Diffusion
The AnomalyXFusion framework comprises two distinct yet synergistic modules: the Multi-modal In-Fusion (MIF) module and the Dynamic Dif-Fusion (DDF) module.
Learning Long-form Video Prior via Generative Pre-Training
Instead of operating on pixel space, it is efficient to employ visual locations like bounding boxes and keypoints to represent key information in videos, which can be simply discretized and then tokenized for consumption by GPT.
GSCo: Towards Generalizable AI in Medicine via Generalist-Specialist Collaboration
Specifically, we propose a cooperative framework, Generalist-Specialist Collaboration (GSCo), which consists of two stages, namely the construction of GFM and specialists, and collaborative inference on downstream tasks.
NeRF2Points: Large-Scale Point Cloud Generation From Street Views' Radiance Field Optimization
Neural Radiance Fields (NeRF) have emerged as a paradigm-shifting methodology for the photorealistic rendering of objects and environments, enabling the synthesis of novel viewpoints with remarkable fidelity.
Self-Supervised Learning for Medical Image Data with Anatomy-Oriented Imaging Planes
In this work, we propose two complementary pretext tasks for this group of medical image data based on the spatial relationship of the imaging planes.
QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge
The challenge focuses on the uncertainty quantification of medical image segmentation which considers the omnipresence of inter-rater variability in imaging datasets.
Federated Modality-specific Encoders and Multimodal Anchors for Personalized Brain Tumor Segmentation
In practice, it is not uncommon that some FL participants only possess a subset of the complete imaging modalities, posing inter-modal heterogeneity as a challenge to effectively training a global model on all participants' data.
Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection
We find that in graph anomaly detection, the homophily distribution differences between different classes are significantly greater than those in homophilic and heterophilic graphs.
MedKP: Medical Dialogue with Knowledge Enhancement and Clinical Pathway Encoding
With appropriate data selection and training techniques, Large Language Models (LLMs) have demonstrated exceptional success in various medical examinations and multiple-choice questions.
Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models
Knowledge graph completion (KGC) is a widely used method to tackle incompleteness in knowledge graphs (KGs) by making predictions for missing links.
Editing Factual Knowledge and Explanatory Ability of Medical Large Language Models
To evaluate the editing impact on the behaviours of LLMs, we propose two model editing studies for medical domain: (1) editing factual knowledge for medical specialization and (2) editing the explanatory ability for complex knowledge.
Biomedical Entity Linking as Multiple Choice Question Answering
Although biomedical entity linking (BioEL) has made significant progress with pre-trained language models, challenges still exist for fine-grained and long-tailed entities.
Large Language Model Distilling Medication Recommendation Model
In this paper, we introduce a novel approach called Large Language Model Distilling Medication Recommendation (LEADER).
ConRF: Zero-shot Stylization of 3D Scenes with Conditioned Radiation Fields
Most of the existing works on arbitrary 3D NeRF style transfer required retraining on each single style condition.
CTNeRF: Cross-Time Transformer for Dynamic Neural Radiance Field from Monocular Video
The goal of our work is to generate high-quality novel views from monocular videos of complex and dynamic scenes.
Tune-An-Ellipse: CLIP Has Potential to Find What You Want
Visual prompting of large vision language models such as CLIP exhibits intriguing zero-shot capabilities.
Going Beyond Multi-Task Dense Prediction with Synergy Embedding Models
Multi-task visual scene understanding aims to leverage the relationships among a set of correlated tasks which are solved simultaneously by embedding them within a uni- fied network.
ViLa-MIL: Dual-scale Vision-Language Multiple Instance Learning for Whole Slide Image Classification
Specifically we propose a dual-scale visual descriptive text prompt based on the frozen large language model (LLM) to boost the performance of VLM effectively.
Large Language Models for Generative Information Extraction: A Survey
Information extraction (IE) aims to extract structural knowledge from plain natural language texts.
Federated Learning via Input-Output Collaborative Distillation
Federated learning (FL) is a machine learning paradigm in which distributed local nodes collaboratively train a central model without sharing individually held private data.
COOPER: Coordinating Specialized Agents towards a Complex Dialogue Goal
In recent years, there has been a growing interest in exploring dialogues with more complex goals, such as negotiation, persuasion, and emotional support, which go beyond traditional service-focused dialogue systems.
Improving Biomedical Entity Linking with Retrieval-enhanced Learning
Biomedical entity linking (BioEL) has achieved remarkable progress with the help of pre-trained language models.
Adversarial Medical Image with Hierarchical Feature Hiding
Interestingly, this vulnerability is a double-edged sword, which can be exploited to hide AEs.
Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retinal OCT Images with Full and Sparse Annotations
Experiments on a synthetic dataset and three public clinical datasets show that our framework can effectively align the B-scans for potential motion correction, and achieves superior performance to state-of-the-art 2D deep learning methods in terms of both layer segmentation accuracy and cross-B-scan 3D continuity in both fully and semi-supervised settings, thus offering more clinical values than previous works.
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.
A Survey of Large Language Models in Medicine: Progress, Application, and Challenge
Therefore, this review aims to provide a detailed overview of the development and deployment of LLMs in medicine, including the challenges and opportunities they face.
When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications
To address these two problems, we propose a novel parameter efficient fine-tuning framework for multi-task medical applications, dubbed as MOELoRA.
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
RSF-Conv: Rotation-and-Scale Equivariant Fourier Parameterized Convolution for Retinal Vessel Segmentation
Retinal vessel segmentation is of great clinical significance for the diagnosis of many eye-related diseases, but it is still a formidable challenge due to the intricate vascular morphology.
UniHead: Unifying Multi-Perception for Detection Heads
The detection head constitutes a pivotal component within object detectors, tasked with executing both classification and localization functions.
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.
Ranked #5 on
Conditional Text-to-Image Synthesis
on COCO-MIG
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.
Large Language Models Leverage External Knowledge to Extend Clinical Insight Beyond Language Boundaries
We systematically evaluate LLMs in the Chinese medical context and develop a novel in-context learning framework to enhance their performance.
TAKT: Target-Aware Knowledge Transfer for Whole Slide Image Classification
Despite incorporating the target features during training, the teacher model tends to overlook them under the inherent domain shift and task discrepancy.
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.
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.
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.
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.
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.
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 #71 on
Semantic Segmentation
on NYU Depth v2
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.
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.
"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.
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.
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.
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.
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.
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 #3 on
Object Detection
on PKU-DDD17-Car
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 mitigate the complexity introduced by the model ensemble, we adopt the teacher-student paradigm, leveraging the diverse outputs from multiple learned networks as supervisory signals to guide the training of the student network.
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 pos