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Found 60 papers, 28 papers with code
MR-based synthetic CT generation using a deep convolutional neural network method
Applying a trained model to generate a complete sCT volume for each new patient MR image only took 9 s, which was much faster than the atlas‐based approach.
Automatic Liver Lesion Segmentation Using A Deep Convolutional Neural Network Method
Liver lesion segmentation is an important step for liver cancer diagnosis, treatment planning and treatment evaluation.
Multi-Perspective Neural Architecture for Recommendation System
In one stage, the user and item are represented from multiple perspectives and in each perspective, the representations of user and item put attentions to each other.
SIMPLE: Statistical Inference on Membership Profiles in Large Networks
Both tests are of the Hotelling-type statistics based on the rows of empirical eigenvectors or their ratios, whose asymptotic covariance matrices are very challenging to derive and estimate.
CreditPrint: Credit Investigation via Geographic Footprints by Deep Learning
Credit investigation is critical for financial services.
Semi-Supervised Deep Learning Using Improved Unsupervised Discriminant Projection
Deep learning demands a huge amount of well-labeled data to train the network parameters.
Covidex: Neural Ranking Models and Keyword Search Infrastructure for the COVID-19 Open Research Dataset
We present Covidex, a search engine that exploits the latest neural ranking models to provide information access to the COVID-19 Open Research Dataset curated by the Allen Institute for AI.
IEEE 802.11be-Wi-Fi 7: New Challenges and Opportunities
Due to the related stringent requirements, supporting these applications over wireless local area network (WLAN) is far beyond the capabilities of the new WLAN standard -- IEEE 802. 11ax.
Microscope Based HER2 Scoring System
Recently, artificial intelligence (AI) has been used in various disease diagnosis to improve diagnostic accuracy and reliability, but the interpretation of diagnosis results is still an open problem.
Federated Crowdsensing: Framework and Challenges
In particular, we first propose a federated crowdsensing framework, which analyzes the privacy concerns of each crowdsensing stage (i. e., task creation, task assignment, task execution, and data aggregation) and discuss how federated learning techniques may take effect.
Label Confusion Learning to Enhance Text Classification Models
LCM can learn label confusion to capture semantic overlap among labels by calculating the similarity between instances and labels during training and generate a better label distribution to replace the original one-hot label vector, thus improving the final classification performance.
Text-Based Person Search with Limited Data
Firstly, to fully utilize the existing small-scale benchmarking datasets for more discriminative feature learning, we introduce a cross-modal momentum contrastive learning framework to enrich the training data for a given mini-batch.
Ranked #10 on
Text based Person Retrieval
on CUHK-PEDES
(using extra training data)
CoDiM: Learning with Noisy Labels via Contrastive Semi-Supervised Learning
Labels are costly and sometimes unreliable.
Ranked #5 on
Image Classification
on mini WebVision 1.0
Label Hierarchy Transition: Delving into Class Hierarchies to Enhance Deep Classifiers
Existing methods commonly address hierarchical classification by decoupling it into a series of multi-class classification tasks.
Data Valuation for Vertical Federated Learning: A Model-free and Privacy-preserving Method
In response, we propose FedValue, a privacy-preserving, task-specific but model-free data valuation method for VFL, which consists of a data valuation metric and a federated computation method.
HyObscure: Hybrid Obscuring for Privacy-Preserving Data Publishing
We then design a novel hybrid protection mechanism called HyObscure, to cross-iteratively optimize the generalization and obfuscation operations for maximum privacy protection under a certain utility guarantee.
Node-Aligned Graph Convolutional Network for Whole-Slide Image Representation and Classification
In this paper, we propose a hierarchical global-to-local clustering strategy to build a Node-Aligned GCN (NAGCN) to represent WSI with rich local structural information as well as global distribution.
UIGR: Unified Interactive Garment Retrieval
In this paper, we propose a Unified Interactive Garment Retrieval (UIGR) framework to unify TGR and VCR.
Deep learning-based approach to reveal tumor mutational burden status from whole slide images across multiple cancer types
MC-TMB algorithm also exhibited good generalization on the external validation cohort with an AUC of 0. 732 (0. 683-0. 761), and better performance when compared to other methods.
Large-Scale Privacy-Preserving Network Embedding against Private Link Inference Attacks
Basically, we propose to perturb the original network by adding or removing links, and expect the embedding generated on the perturbed network can leak little information about private links but hold high utility for various downstream tasks.
FashionViL: Fashion-Focused Vision-and-Language Representation Learning
We thus propose a Multi-View Contrastive Learning task for pulling closer the visual representation of one image to the compositional multimodal representation of another image+text.
Large-Scale Product Retrieval with Weakly Supervised Representation Learning
Large-scale weakly supervised product retrieval is a practically useful yet computationally challenging problem.
NMR spectrum reconstruction as a pattern recognition problem
A new deep neural network based on the WaveNet architecture (WNN) is presented, which is designed to grasp specific patterns in the NMR spectra.
Safety and Performance, Why not Both? Bi-Objective Optimized Model Compression toward AI Software Deployment
By simulating the attack mechanism as the safety test, SafeCompress can automatically compress a big model to a small one following the dynamic sparse training paradigm.
DeepNoise: Signal and Noise Disentanglement based on Classifying Fluorescent Microscopy Images via Deep Learning
The high-content image-based assay is commonly leveraged for identifying the phenotypic impact of genetic perturbations in biology field.
RAPO: An Adaptive Ranking Paradigm for Bilingual Lexicon Induction
Bilingual lexicon induction induces the word translations by aligning independently trained word embeddings in two languages.
LiCamGait: Gait Recognition in the Wild by Using LiDAR and Camera Multi-modal Visual Sensors
LiDAR can capture accurate depth information in large-scale scenarios without the effect of light conditions, and the captured point cloud contains gait-related 3D geometric properties and dynamic motion characteristics.
Adap DP-FL: Differentially Private Federated Learning with Adaptive Noise
The heterogeneity and convergence of training parameters were simply not considered.
Resolution enhancement of NMR by decoupling with low-rank Hankel model
Nuclear magnetic resonance (NMR) spectroscopy has become a formidable tool for biochemistry and medicine.
RLogist: Fast Observation Strategy on Whole-slide Images with Deep Reinforcement Learning
Most of the existing methods rely on a multiple instance learning framework that requires densely sampling local patches at high magnification.
On Root Cause Localization and Anomaly Mitigation through Causal Inference
After that, we further propose an anomaly mitigation approach that aims to recommend mitigation actions on abnormal features to revert the abnormal outcomes such that the counterfactuals guided by the causal mechanism are normal.
A Unified Knowledge Graph Augmentation Service for Boosting Domain-specific NLP Tasks
We propose KnowledgeDA, a unified domain language model development service to enhance the task-specific training procedure with domain knowledge graphs.
Controllable Person Image Synthesis with Pose-Constrained Latent Diffusion
Controllable person image synthesis aims at rendering a source image based on user-specified changes in body pose or appearance.
How To Prevent the Poor Performance Clients for Personalized Federated Learning?
Intuitively, these poor clients may come from biased universal information shared with others.
Artificial intelligence for diagnosing and predicting survival of patients with renal cell carcinoma: Retrospective multi-center study
The OSrisk for the prediction of 5-year survival status achieved AUC of 0. 784 (0. 746-0. 819) in the TCGA cohort, which was further verified in the independent General cohort and the CPTAC cohort, with AUC of 0. 774 (0. 723-0. 820) and 0. 702 (0. 632-0. 765), respectively.
Cross-center Early Sepsis Recognition by Medical Knowledge Guided Collaborative Learning for Data-scarce Hospitals
In this paper, for a common disease in ICU patients, sepsis, we propose a novel cross-center collaborative learning framework guided by medical knowledge, SofaNet, to achieve early recognition of this disease.
Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networks
In order to improve the information-sharing capability and innovation of various healthcare-related institutions, and then to establish a next-generation open medical collaboration network, we propose a unified framework for vertical federated knowledge transfer mechanism (VFedTrans) based on a novel cross-hospital representation distillation component.
Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading
We assessed the effectiveness of FACL in cancer diagnosis and Gleason grading tasks using 19, 461 whole-slide images of prostate cancer from multiple centers.
Slate-Aware Ranking for Recommendation
Therefore, the ranking stage is still essential for most applications to provide high-quality candidate set for the re-ranking stage.
FAME-ViL: Multi-Tasking Vision-Language Model for Heterogeneous Fashion Tasks
In the fashion domain, there exists a variety of vision-and-language (V+L) tasks, including cross-modal retrieval, text-guided image retrieval, multi-modal classification, and image captioning.
Achieving Counterfactual Fairness for Anomaly Detection
Ensuring fairness in anomaly detection models has received much attention recently as many anomaly detection applications involve human beings.
RLTF: Reinforcement Learning from Unit Test Feedback
The goal of program synthesis, or code generation, is to generate executable code based on given descriptions.
IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models
Despite the simplicity of our method, an IP-Adapter with only 22M parameters can achieve comparable or even better performance to a fully fine-tuned image prompt model.
Ranked #3 on
Diffusion Personalization Tuning Free
on AgeDB
Dynamic Low-Rank Instance Adaptation for Universal Neural Image Compression
We thus introduce a dynamic gating network on top of the low-rank adaptation method, in order to decide which decoder layer should employ adaptation.
VIGC: Visual Instruction Generation and Correction
A practical solution to this problem would be to utilize the available multimodal large language models (MLLMs) to generate instruction data for vision-language tasks.
Towards Real-Time Neural Video Codec for Cross-Platform Application Using Calibration Information
First, to solve the problem of inconsistency of codec caused by the uncertainty of floating point calculations across platforms, we design a calibration transmitting system to guarantee the consistent quantization of entropy parameters between the encoding and decoding stages.
LogGPT: Log Anomaly Detection via GPT
However, there is a gap between language modeling and anomaly detection as the objective of training a sequential model via a language modeling loss is not directly related to anomaly detection.
Algorithmic Recourse for Anomaly Detection in Multivariate Time Series
Anomaly detection in multivariate time series has received extensive study due to the wide spectrum of applications.
Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion Models
Specifically, in the first stage, we design a simple prior conditional diffusion model that predicts the global features of the target image by mining the global alignment relationship between pose coordinates and image appearance.
Effortless Cross-Platform Video Codec: A Codebook-Based Method
Due to the absence of autoregressive modeling and optical flow alignment, we can design an extremely minimalist framework that can greatly benefit computational efficiency.
Image Super-resolution Via Latent Diffusion: A Sampling-space Mixture Of Experts And Frequency-augmented Decoder Approach
To alleviate the huge computational cost required by pixel-based diffusion SR, latent-based methods utilize a feature encoder to transform the image and then implement the SR image generation in a compact latent space.
From Authority-Respect to Grassroots-Dissent: Degree-Weighted Social Learning and Convergence Speed
Opinions are influenced by neighbors, with varying degrees of emphasis based on their connections.
Safety and Performance, Why Not Both? Bi-Objective Optimized Model Compression against Heterogeneous Attacks Toward AI Software Deployment
To mitigate this issue, AI software compression plays a crucial role, which aims to compress model size while keeping high performance.
Graph Contrastive Learning with Cohesive Subgraph Awareness
However, such stochastic augmentations may severely damage the intrinsic properties of a graph and deteriorate the following representation learning process.
LiveHPS: LiDAR-based Scene-level Human Pose and Shape Estimation in Free Environment
For human-centric large-scale scenes, fine-grained modeling for 3D human global pose and shape is significant for scene understanding and can benefit many real-world applications.
A Unified Framework for Human-centric Point Cloud Video Understanding
Human-centric Point Cloud Video Understanding (PVU) is an emerging field focused on extracting and interpreting human-related features from sequences of human point clouds, further advancing downstream human-centric tasks and applications.
WiTUnet: A U-Shaped Architecture Integrating CNN and Transformer for Improved Feature Alignment and Local Information Fusion
Low-dose computed tomography (LDCT) has become the technology of choice for diagnostic medical imaging, given its lower radiation dose compared to standard CT, despite increasing image noise and potentially affecting diagnostic accuracy.
Weakly Supervised Semantic Segmentation with Boundary Exploration
Weakly supervised semantic segmentation with image-level labels has attracted a lot of attention recently because these labels are already available in most datasets.
Leveraging Bidding Graphs for Advertiser-Aware Relevance Modeling in Sponsored Search
As the fundamental basis of sponsored search, relevance modeling has attracted increasing attention due to the tremendous practical value.
Unsupervised Adverbial Identification in Modern Chinese Literature
In Chinese, the derivation may be marked either with the standard adverbial marker DI, or the non-standard marker DE.
