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Found 32 papers, 7 papers with code
MDCSpell: A Multi-task Detector-Corrector Framework for Chinese Spelling Correction
Chinese Spelling Correction (CSC) is a task to detect and correct misspelled characters in Chinese texts.
Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge
The online validation set incorporated 52 HRCT scans from patients with fibrotic lung disease and the offline test set included 140 cases from fibrosis and COVID-19 patients.
Mechanical Characterization and Inverse Design of Stochastic Architected Metamaterials Using Neural Operators
Our work marks a significant advancement in the field of materials-by-design, potentially heralding a new era in the discovery and development of next-generation metamaterials with unparalleled mechanical characteristics derived directly from experimental insights.
Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models
Financial sentiment analysis is critical for valuation and investment decision-making.
Post-Hoc Explainability of BI-RADS Descriptors in a Multi-task Framework for Breast Cancer Detection and Segmentation
Despite recent medical advancements, breast cancer remains one of the most prevalent and deadly diseases among women.
Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models
Sentiment analysis is a vital tool for uncovering insights from financial articles, news, and social media, shaping our understanding of market movements.
Kinship can hinder cooperation in heterogeneous populations
Two sets of interrelated results are established: a set of analytical results focusing on the subgame perfect equilibrium and a set of agent-based simulation results based on an evolutionary game model.
Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation
In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution.
Data-aware customization of activation functions reduces neural network error
Activation functions play critical roles in neural networks, yet current off-the-shelf neural networks pay little attention to the specific choice of activation functions used.
High-Quality Real-Time Rendering Using Subpixel Sampling Reconstruction
Generating high-quality, realistic rendering images for real-time applications generally requires tracing a few samples-per-pixel (spp) and using deep learning-based approaches to denoise the resulting low-spp images.
Detachable Novel Views Synthesis of Dynamic Scenes Using Distribution-Driven Neural Radiance Fields
Specifically, it employs a neural representation to capture the scene distribution in the static background and a 6D-input NeRF to represent dynamic objects, respectively.
TextRGNN: Residual Graph Neural Networks for Text Classification
Recently, text classification model based on graph neural network (GNN) has attracted more and more attention.
Auto-Encoding Score Distribution Regression for Action Quality Assessment
Traditionally, AQA is treated as a regression problem to learn the underlying mappings between videos and action scores.
Ranked #1 on
Action Quality Assessment
on JIGSAWS
BI-RADS-Net: An Explainable Multitask Learning Approach for Cancer Diagnosis in Breast Ultrasound Images
In healthcare, it is essential to explain the decision-making process of machine learning models to establish the trustworthiness of clinicians.
Understanding Diversity Based Neural Network Pruning in Teacher Student Setup
Despite multitude of empirical advances, there is a lack of theoretical understanding of the effectiveness of different pruning methods.
Diversity Based Edge Pruning of Neural Networks Using Determinantal Point Processes
We then empirically show that DPP edge pruning for neural networks outperforms other competing methods (both edge and node) on real data.
Predicting Material Properties Using a 3D Graph Neural Network with Invariant Local Descriptors
Accurate prediction of physical properties is critical for discovering and designing novel materials.
A Use of Even Activation Functions in Neural Networks
The key to success is to effectively integrate existing knowledge or hypotheses about the underlying structure of the data into the architecture of deep learning models to guide machine learning.
Detecting Individuals with Depressive Disorder fromPersonal Google Search and YouTube History Logs
Depressive disorder is one of the most prevalent mental illnesses among the global population.
Inference on tissue transplantation experiments
This method can provide the most probable results of a group of experiments or the probability of a specific result for each experiment.
The Relationship between Deteriorating Mental Health Conditions and Longitudinal Behavioral Changes in Google and YouTube Usages among College Students in the United States during COVID-19: Observational Study
The goal of this study is to examine, among college students, the relationship between deteriorating mental health conditions and changes in user behaviors when engaging with Google Search and YouTube during COVID-19.
A Novel DNN Training Framework via Data Sampling and Multi-Task Optimization
The training set is used for training the model while the validation set is used to estimate the generalization performance of the trained model as the training proceeds to avoid over-fitting.
Statistical Mechanical Analysis of Neural Network Pruning
We inspect different pruning techniques under the statistical mechanics formulation of a teacher-student framework and derive their generalization error (GE) bounds.
Breast Anatomy Enriched Tumor Saliency Estimation
Then we refine the layers by integrating a non-semantic breast anatomy model to solve the problems of incomplete mammary layers.
Fuzzy Semantic Segmentation of Breast Ultrasound Image with Breast Anatomy Constraints
Due to low cost, portable, no radiation, and high efficiency, breast ultrasound (BUS) imaging is the most popular approach for diagnosing early breast cancer.
Efficient Inference of CNNs via Channel Pruning
The deployment of Convolutional Neural Networks (CNNs) on resource constrained platforms such as mobile devices and embedded systems has been greatly hindered by their high implementation cost, and thus motivated a lot research interest in compressing and accelerating trained CNN models.
Tumor Saliency Estimation for Breast Ultrasound Images via Breast Anatomy Modeling
First, we model breast anatomy and decompose breast ultrasound image into layers using Neutro-Connectedness; then utilize the layers to generate the foreground and background maps; and finally propose a novel objective function to estimate the tumor saliency by integrating the foreground map, background map, adaptive center bias, and region-based correlation cues.
A Mixture of Expert Approach for Low-Cost Customization of Deep Neural Networks
Since the customized dataset is in general very small, the cost of training LE and GN would be much lower than that of re-training of GE.
A Hybrid Framework for Tumor Saliency Estimation
Automatic tumor segmentation of breast ultrasound (BUS) image is quite challenging due to the complicated anatomic structure of breast and poor image quality.
Computer-Aided Knee Joint Magnetic Resonance Image Segmentation - A Survey
In this survey paper, we classify the existing methods by their principles and discuss the current research status and point out the future research trend in-depth.
BUSIS: A Benchmark for Breast Ultrasound Image Segmentation
Breast ultrasound (BUS) image segmentation is challenging and critical for BUS Comput-er-Aided Diagnosis (CAD) systems.
Automatic Breast Ultrasound Image Segmentation: A Survey
Breast cancer is one of the leading causes of cancer death among women worldwide.
