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Found 43 papers, 13 papers with code
Constructing Uyghur Name Entity Recognition System using Neural Machine Translation Tag Projection
Although named entity recognition achieved great success by introducing the neural networks, it is challenging to apply these models to low resource languages including Uyghur while it depends on a large amount of annotated training data.
DeltaNet: Conditional Medical Report Generation for COVID-19 Diagnosis
To reduce the workload of radiologists, we propose DeltaNet to generate medical reports automatically.
SAMDA: Leveraging SAM on Few-Shot Domain Adaptation for Electronic Microscopy Segmentation
It has been shown that traditional deep learning methods for electronic microscopy segmentation usually suffer from low transferability when samples and annotations are limited, while large-scale vision foundation models are more robust when transferring between different domains but facing sub-optimal improvement under fine-tuning.
Exploring General Intelligence via Gated Graph Transformer in Functional Connectivity Studies
Functional connectivity (FC) as derived from fMRI has emerged as a pivotal tool in elucidating the intricacies of various psychiatric disorders and delineating the neural pathways that underpin cognitive and behavioral dynamics inherent to the human brain.
Robust Multidimentional Chinese Remainder Theorem for Integer Vector Reconstruction
To address this problem, a robust MD Chinese remainder theorem (CRT) was recently proposed for a special class of moduli, where the remaining integer matrices left-divided by a greatest common left divisor (gcld) of all the moduli are pairwise commutative and coprime.
CDR-Adapter: Learning Adapters to Dig Out More Transferring Ability for Cross-Domain Recommendation Models
In this paper, we present a scalable and efficient paradigm to address data sparsity and cold-start issues in CDR, named CDR-Adapter, by decoupling the original recommendation model from the mapping function, without requiring re-engineering the network structure.
PhantomSound: Black-Box, Query-Efficient Audio Adversarial Attack via Split-Second Phoneme Injection
We significantly enhance the query efficiency and reduce the cost of a successful untargeted and targeted adversarial attack by 93. 1% and 65. 5% compared with the state-of-the-art black-box attacks, using merely ~300 queries (~5 minutes) and ~1, 500 queries (~25 minutes), respectively.
MASTERKEY: Practical Backdoor Attack Against Speaker Verification Systems
In this work, we propose a backdoor attack MASTERKEY, to compromise the SV models.
BEVStereo++: Accurate Depth Estimation in Multi-view 3D Object Detection via Dynamic Temporal Stereo
Bounded by the inherent ambiguity of depth perception, contemporary multi-view 3D object detection methods fall into the performance bottleneck.
DeltaNet:Conditional Medical Report Generation for COVID-19 Diagnosis
To reduce the workload of radiologists, we propose DeltaNet to generate medical reports automatically.
FreeVC: Towards High-Quality Text-Free One-Shot Voice Conversion
Voice conversion (VC) can be achieved by first extracting source content information and target speaker information, and then reconstructing waveform with these information.
Energy-grade double pricing mechanism for a combined heat and power system using the asynchronous dispatch method
The problem of heat and electricity pricing in combined heat and power systems regarding the time scales of electricity and heat, as well as thermal energy quality, is studied.
A Multiple Market Trading Mechanism for Electricity, Renewable Energy Certificate and Carbon Emission Right of Virtual Power Plants
With the introduction of the inventory mechanism of REC and CER, the profit of the VPP increases and better trading decisions with multiple markets are made under the requirements of renewable portfolio standard (RPS) and carbon emission (CE) quota requirements.
SuperVoice: Text-Independent Speaker Verification Using Ultrasound Energy in Human Speech
Our evaluation shows that SUPERVOICE achieves 0. 58% equal error rate in the speaker verification task, it only takes 120 ms for testing an incoming utterance, outperforming all existing speaker verification systems.
CCAT-NET: A Novel Transformer Based Semi-supervised Framework for Covid-19 Lung Lesion Segmentation
In this work, we propose a novel network structure that combines CNN and Transformer for the segmentation of COVID-19 lesions.
i-Razor: A Differentiable Neural Input Razor for Feature Selection and Dimension Search in DNN-Based Recommender Systems
Noisy features and inappropriate embedding dimension assignments can deteriorate the performance of recommender systems and introduce unnecessary complexity in model training and online serving.
Learning Incrementally to Segment Multiple Organs in a CT Image
In each incremental learning (IL) stage, we lose the access to previous data and annotations, whose knowledge is assumingly captured by the current model, and gain the access to a new dataset with annotations of new organ categories, from which we learn to update the organ segmentation model to include the new organs.
CLS: Cross Labeling Supervision for Semi-Supervised Learning
Based on FixMatch, where a pseudo label is generated from a weakly-augmented sample to teach the prediction on a strong augmentation of the same input sample, CLS allows the creation of both pseudo and complementary labels to support both positive and negative learning.
Knowledge Matters: Radiology Report Generation with General and Specific Knowledge
In this paper, we propose a knowledge-enhanced radiology report generation approach introduces two types of medical knowledge: 1) General knowledge, which is input independent and provides the broad knowledge for report generation; 2) Specific knowledge, which is input dependent and provides the fine-grained knowledge for report generation.
Radiology Report Generation with a Learned Knowledge Base and Multi-modal Alignment
In clinics, a radiology report is crucial for guiding a patient's treatment.
IGrow: A Smart Agriculture Solution to Autonomous Greenhouse Control
However, the optimal control of autonomous greenhouses is challenging, requiring decision-making based on high-dimensional sensory data, and the scaling of production is limited by the scarcity of labor capable of handling this task.
Sample Efficient Reinforcement Learning via Model-Ensemble Exploration and Exploitation
Model-based deep reinforcement learning has achieved success in various domains that require high sample efficiencies, such as Go and robotics.
AMA-GCN: Adaptive Multi-layer Aggregation Graph Convolutional Network for Disease Prediction
The encoder can automatically construct the population graph using phenotypic measures which have a positive impact on the final results, and further realizes the fusion of multimodal information.
Pathological Image Segmentation with Noisy Labels
Segmentation of pathological images is essential for accurate disease diagnosis.
You Only Learn Once: Universal Anatomical Landmark Detection
However, all of those methods are unary in the sense that a highly specialized network is trained for a single task say associated with a particular anatomical region.
One-Shot Medical Landmark Detection
The success of deep learning methods relies on the availability of a large number of datasets with annotations; however, curating such datasets is burdensome, especially for medical images.
Incremental Learning for Multi-organ Segmentation with Partially Labeled Datasets
In each IL stage, we lose access to the previous annotations, whose knowledge is assumingly captured by the current model, and gain the access to a new dataset with annotations of new organ categories, from which we learn to update the organ segmentation model to include the new organs.
Noise Optimization for Artificial Neural Networks
Adding noises to artificial neural network(ANN) has been shown to be able to improve robustness in previous work.
Ensemble manifold based regularized multi-modal graph convolutional network for cognitive ability prediction
Methods: To take advantage of complementary information from multi-modal fMRI, we propose an interpretable multi-modal graph convolutional network (MGCN) model, incorporating the fMRI time series and the functional connectivity (FC) between each pair of brain regions.
Deep Learning to Segment Pelvic Bones: Large-scale CT Datasets and Baseline Models
Due to the lack of a large-scale pelvic CT dataset with annotations, deep learning methods are not fully explored.
G-RCN: Optimizing the Gap between Classification and Localization Tasks for Object Detection
By comparing the performance between the original Faster R-CNN and that with partially separated feature maps, we show that: (1) Sharing high-level features for the classification and localization tasks is sub-optimal; (2) Large stride is beneficial for classification but harmful for localization; (3) Global context information could improve the performance of classification.
Distance Correlation Based Brain Functional Connectivity Estimation and Non-Convex Multi-Task Learning for Developmental fMRI Studies
Resting-state functional magnetic resonance imaging (rs-fMRI)-derived functional connectivity patterns have been extensively utilized to delineate global functional organization of the human brain in health, development, and neuropsychiatric disorders.
Label-Free Segmentation of COVID-19 Lesions in Lung CT
Scarcity of annotated images hampers the building of automated solution for reliable COVID-19 diagnosis and evaluation from CT. To alleviate the burden of data annotation, we herein present a label-free approach for segmenting COVID-19 lesions in CT via pixel-level anomaly modeling that mines out the relevant knowledge from normal CT lung scans.
Marginal loss and exclusion loss for partially supervised multi-organ segmentation
Annotating multiple organs in medical images is both costly and time-consuming; therefore, existing multi-organ datasets with labels are often low in sample size and mostly partially labeled, that is, a dataset has a few organs labeled but not all organs.
DeepACC:Automate Chromosome Classification based on Metaphase Images using Deep Learning Framework Fused with Prior Knowledge
Previous methods only classify manually segmented single chromosome, which is far from clinical practice.
Causal inference of brain connectivity from fMRI with $ψ$-Learning Incorporated Linear non-Gaussian Acyclic Model ($ψ$-LiNGAM)
A popular definition of FC is by statistical associations between measured brain regions.
PBRnet: Pyramidal Bounding Box Refinement to Improve Object Localization Accuracy
Based on these principles, we designed a novel boundary refinement architecture to improve localization accuracy by combining coarse-to-fine framework with feature pyramid structure, named as Pyramidal Bounding Box Refinement network(PBRnet), which parameterizes gradually focused boundary areas of objects and leverages lower-level feature maps to extract finer local information when refining the predicted bounding boxes.
Learning from Suspected Target: Bootstrapping Performance for Breast Cancer Detection in Mammography
It is worth mention that dense breast typically has a higher risk for developing breast cancers and also are harder for cancer detection in diagnosis, and our method outperforms a reported result from performance of radiologists.
Causality based Feature Fusion for Brain Neuro-Developmental Analysis
Second, we used causality values as the weight for the directional connectivity between brain regions.
DeepACEv2: Automated Chromosome Enumeration in Metaphase Cell Images Using Deep Convolutional Neural Networks
Chromosome enumeration is an essential but tedious procedure in karyotyping analysis.
Training Artificial Neural Networks by Generalized Likelihood Ratio Method: Exploring Brain-like Learning to Improve Robustness
In this work, we propose a generalized likelihood ratio method capable of training the artificial neural networks with some biological brain-like mechanisms,. e. g., (a) learning by the loss value, (b) learning via neurons with discontinuous activation and loss functions.
A Span-Extraction Dataset for Chinese Machine Reading Comprehension
Machine Reading Comprehension (MRC) has become enormously popular recently and has attracted a lot of attention.
Radial Velocity Retrieval for Multichannel SAR Moving Targets with Time-Space Doppler De-ambiguity
Accordingly, the multichannel SAR systems with different parameters are investigated in three different cases with diverse Doppler ambiguity properties, and a multi-frequency SAR is then proposed to obtain the RV estimation by solving the ambiguity problem based on Chinese remainder theorem (CRT).
