Search Results for author:
Found 16 papers, 7 papers with code
Re-Examine Distantly Supervised NER: A New Benchmark and a Simple Approach
This paper delves into Named Entity Recognition (NER) under the framework of Distant Supervision (DS-NER), where the main challenge lies in the compromised quality of labels due to inherent errors such as false positives, false negatives, and positive type errors.
Training-free image style alignment for self-adapting domain shift on handheld ultrasound devices
Moreover, the models trained on standard ultrasound device data are constrained by training data distribution and perform poorly when directly applied to handheld device data.
Improving Unsupervised Relation Extraction by Augmenting Diverse Sentence Pairs
We also identify the limitation of noise-contrastive estimation (NCE) loss for relation representation learning and propose to apply margin loss for sentence pairs.
LLMRec: Benchmarking Large Language Models on Recommendation Task
The benchmark results indicate that LLMs displayed only moderate proficiency in accuracy-based tasks such as sequential and direct recommendation.
Relation-Aware Network with Attention-Based Loss for Few-Shot Knowledge Graph Completion
Specifically, to better utilize the plentiful negative samples and alleviate the zero-loss issue, we strategically select relevant negative samples and design an attention-based loss function to further differentiate the importance of each negative sample.
Is ChatGPT a Good Recommender? A Preliminary Study
We conduct human evaluations on two explainability-oriented tasks to more accurately evaluate the quality of contents generated by different models.
VertMatch: A Semi-supervised Framework for Vertebral Structure Detection in 3D Ultrasound Volume
We propose VertMatch, a two-step framework to detect vertebral structures in 3D ultrasound volume by utilizing unlabeled data in semi-supervised manner.
Improving Distantly Supervised Relation Extraction by Natural Language Inference
To reduce human annotations for relation extraction (RE) tasks, distantly supervised approaches have been proposed, while struggling with low performance.
Distantly Supervised Named Entity Recognition via Confidence-Based Multi-Class Positive and Unlabeled Learning
In this paper, we study the named entity recognition (NER) problem under distant supervision.
Proxy-bridged Image Reconstruction Network for Anomaly Detection in Medical Images
To mitigate this problem, in this paper, we propose a novel Proxy-bridged Image Reconstruction Network (ProxyAno) for anomaly detection in medical images.
Automatic segmentation of vertebral features on ultrasound spine images using Stacked Hourglass Network
Methods: The network was trained to detect vertebral SP and laminae as five landmarks on 1200 ultrasound transverse images and validated on 100 images.
Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Images
In the end, we further utilize the reconstructed image to extract the structure and measure the difference between structure extracted from original and the reconstructed image.
BioNet: Infusing Biomarker Prior into Global-to-Local Network for Choroid Segmentation in Optical Coherence Tomography Images
Firstly, our method trains a biomarker prediction network to learn the features of the biomarker.
Sparse-GAN: Sparsity-constrained Generative Adversarial Network for Anomaly Detection in Retinal OCT Image
With the development of convolutional neural network, deep learning has shown its success for retinal disease detection from optical coherence tomography (OCT) images.
CE-Net: Context Encoder Network for 2D Medical Image Segmentation
In this paper, we propose a context encoder network (referred to as CE-Net) to capture more high-level information and preserve spatial information for 2D medical image segmentation.
Ranked #1 on
Optic Disc Segmentation
on Messidor
Multi-Cell Multi-Task Convolutional Neural Networks for Diabetic Retinopathy Grading
To considering the relationships of images with different stages, we propose a \textbf{Multi-Task} learning strategy which predicts the label with both classification and regression.
