Search Results for author:
Found 15 papers, 8 papers with code
AnyECG: Foundational Models for Electrocardiogram Analysis
Electrocardiogram (ECG), a non-invasive and affordable tool for cardiac monitoring, is highly sensitive in detecting acute heart attacks.
Fair Evaluation of Federated Learning Algorithms for Automated Breast Density Classification: The Results of the 2022 ACR-NCI-NVIDIA Federated Learning Challenge
The correct interpretation of breast density is important in the assessment of breast cancer risk.
Personalized Heart Disease Detection via ECG Digital Twin Generation
Heart diseases rank among the leading causes of global mortality, demonstrating a crucial need for early diagnosis and intervention.
PoCo: A Self-Supervised Approach via Polar Transformation Based Progressive Contrastive Learning for Ophthalmic Disease Diagnosis
In this paper, we present a self-supervised method via polar transformation based progressive contrastive learning, called PoCo, for ophthalmic disease diagnosis.
Arithmetic Feature Interaction Is Necessary for Deep Tabular Learning
Our extensive experiments on real-world data also validate the consistent effectiveness, efficiency, and rationale of AMFormer, suggesting it has established a strong inductive bias for deep learning on tabular data.
Joint Explicit and Implicit Cross-Modal Interaction Network for Anterior Chamber Inflammation Diagnosis
To this end, we propose a jointly Explicit and implicit Cross-Modal Interaction Network (EiCI-Net) for Anterior Chamber Inflammation Diagnosis that uses anterior segment optical coherence tomography (AS-OCT) images, slit-lamp images, and clinical data jointly.
TSegFormer: 3D Tooth Segmentation in Intraoral Scans with Geometry Guided Transformer
Optical Intraoral Scanners (IOS) are widely used in digital dentistry to provide detailed 3D information of dental crowns and the gingiva.
M$^3$CS: Multi-Target Masked Point Modeling with Learnable Codebook and Siamese Decoders
Specifically, with masked point cloud as input, M$^3$CS introduces two decoders to predict masked representations and the original points simultaneously.
SelFLoc: Selective Feature Fusion for Large-scale Point Cloud-based Place Recognition
To enhance message passing along particular axes, Stacked Asymmetric Convolution Block (SACB) is designed, which is one of the main contributions in this paper.
Robust Image Ordinal Regression with Controllable Image Generation
Image ordinal regression has been mainly studied along the line of exploiting the order of categories.
CTT-Net: A Multi-view Cross-token Transformer for Cataract Postoperative Visual Acuity Prediction
Clinically, to assess the necessity of cataract surgery, accurately predicting postoperative VA before surgery by analyzing multi-view optical coherence tomography (OCT) images is crucially needed.
Robust Training of Graph Neural Networks via Noise Governance
To address these issues, we propose a novel RTGNN (Robust Training of Graph Neural Networks via Noise Governance) framework that achieves better robustness by learning to explicitly govern label noise.
ME-GAN: Learning Panoptic Electrocardio Representations for Multi-view ECG Synthesis Conditioned on Heart Diseases
Electrocardiogram (ECG) is a widely used non-invasive diagnostic tool for heart diseases.
Spatial Object Recommendation with Hints: When Spatial Granularity Matters
We design a novel multi-task learning model called MPR (short for Multi-level POI Recommendation), where each task aims to return the top-k POIs at a certain spatial granularity level.
Improving Automatic Source Code Summarization via Deep Reinforcement Learning
To the best of our knowledge, most state-of-the-art approaches follow an encoder-decoder framework which encodes the code into a hidden space and then decode it into natural language space, suffering from two major drawbacks: a) Their encoders only consider the sequential content of code, ignoring the tree structure which is also critical for the task of code summarization, b) Their decoders are typically trained to predict the next word by maximizing the likelihood of next ground-truth word with previous ground-truth word given.
