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Found 10 papers, 3 papers with code
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)
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.
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.
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.
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.
