Search Results for author:
Found 19 papers, 6 papers with code
Apollo's Oracle: Retrieval-Augmented Reasoning in Multi-Agent Debates
Addressing the challenge of cognitive constraints, we introduce a novel framework, the Multi-Agent Debate with Retrieval Augmented (MADRA).
DED: Diagnostic Evidence Distillation for acne severity grading on face images
In this study, we propose an acne diagnosis method, Diagnostic Evidence Distillation (DED), that suitably adapts the characteristics of acne diagnosis and can be applied to diagnose under different acne criteria.
Ranked #1 on
Acne Severity Grading
on ACNE04
(Accuracy metric)
Acne Severity Grading on Face Images via Extraction and Guidance of Prior Knowledge
Acne Vulgaris seriously affects people’s daily life.
Ranked #2 on
Acne Severity Grading
on ACNE04
(Accuracy metric)
Causal Coupled Mechanisms: A Control Method with Cooperation and Competition for Complex System
Complex systems are ubiquitous in the real world and tend to have complicated and poorly understood dynamics.
KIEGLFN: A unified acne grading framework on face images
Methods: A unified acne grading framework that can be generalized to apply referring to different grading criteria is developed.
Ranked #3 on
Acne Severity Grading
on ACNE04
(Accuracy metric)
An Acne Grading Framework on Face Images via Skin Attention and SFNet
In this paper, the global estimation of acne severity grading is studied by Convolutional Neural Networks (CNNs) and a unified acne grading framework that can diagnose referring to different grading criteria is proposed.
Ranked #3 on
Acne Severity Grading
on ACNE04
(Accuracy metric)
AdvPicker: Effectively Leveraging Unlabeled Data via Adversarial Discriminator for Cross-Lingual NER
Neural methods have been shown to achieve high performance in Named Entity Recognition (NER), but rely on costly high-quality labeled data for training, which is not always available across languages.
Do recommender systems function in the health domain: a system review
Recommender systems have fulfilled an important role in everyday life.
Medical Knowledge Embedding Based on Recursive Neural Network for Multi-Disease Diagnosis
The representation of knowledge based on first-order logic captures the richness of natural language and supports multiple probabilistic inference models.
Convolutional Gated Recurrent Units for Medical Relation Classification
Convolutional neural network (CNN) and recurrent neural network (RNN) models have become the mainstream methods for relation classification.
Classifying medical relations in clinical text via convolutional neural networks
Deep learning research on relation classification has achieved solid performance in the general domain.
Classification of entities via their descriptive sentences
Hypernym identification of open-domain entities is crucial for taxonomy construction as well as many higher-level applications.
EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning
Our objective is a general system that can extract and represent these knowledge contained in EMRs to support three CDS tasks: test recommendation, initial diagnosis, and treatment plan recommendation, with the given condition of one patient.
Constructing a Hierarchical User Interest Structure based on User Profiles
In this study, we constructed the user interest hierarchy via user profiles.
De-identification of medical records using conditional random fields and long short-term memory networks
The CEGS N-GRID 2016 Shared Task 1 in Clinical Natural Language Processing focuses on the de-identification of psychiatric evaluation records.
Learning and inference in knowledge-based probabilistic model for medical diagnosis
Based on a weighted knowledge graph to represent first-order knowledge and combining it with a probabilistic model, we propose a methodology for the creation of a medical knowledge network (MKN) in medical diagnosis.
Developing a cardiovascular disease risk factor annotated corpus of Chinese electronic medical records
To the best of our knowledge, this is the first annotated corpus concerning CVD risk factors in CEMRs and the guidelines for capturing CVD risk factor annotations from CEMRs were proposed.
Building a comprehensive syntactic and semantic corpus of Chinese clinical texts
Objective: To build a comprehensive corpus covering syntactic and semantic annotations of Chinese clinical texts with corresponding annotation guidelines and methods as well as to develop tools trained on the annotated corpus, which supplies baselines for research on Chinese texts in the clinical domain.
