Deep Reinforcement Learning Framework for Thoracic Diseases
Classification via Prior Knowledge Guidance
- URL: http://arxiv.org/abs/2306.01232v1
- Date: Fri, 2 Jun 2023 01:46:31 GMT
- Title: Deep Reinforcement Learning Framework for Thoracic Diseases
Classification via Prior Knowledge Guidance
- Authors: Weizhi Nie, Chen Zhang, Dan Song, Lina Zhao, Yunpeng Bai, Keliang Xie,
Anan Liu
- Abstract summary: The scarcity of labeled data for related diseases poses a huge challenge to an accurate diagnosis.
We propose a novel deep reinforcement learning framework, which introduces prior knowledge to direct the learning of diagnostic agents.
Our approach's performance was demonstrated using the well-known NIHX-ray 14 and CheXpert datasets.
- Score: 49.87607548975686
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The chest X-ray is often utilized for diagnosing common thoracic diseases. In
recent years, many approaches have been proposed to handle the problem of
automatic diagnosis based on chest X-rays. However, the scarcity of labeled
data for related diseases still poses a huge challenge to an accurate
diagnosis. In this paper, we focus on the thorax disease diagnostic problem and
propose a novel deep reinforcement learning framework, which introduces prior
knowledge to direct the learning of diagnostic agents and the model parameters
can also be continuously updated as the data increases, like a person's
learning process. Especially, 1) prior knowledge can be learned from the
pre-trained model based on old data or other domains' similar data, which can
effectively reduce the dependence on target domain data, and 2) the framework
of reinforcement learning can make the diagnostic agent as exploratory as a
human being and improve the accuracy of diagnosis through continuous
exploration. The method can also effectively solve the model learning problem
in the case of few-shot data and improve the generalization ability of the
model. Finally, our approach's performance was demonstrated using the
well-known NIH ChestX-ray 14 and CheXpert datasets, and we achieved competitive
results. The source code can be found here:
\url{https://github.com/NeaseZ/MARL}.
Related papers
- SynthEnsemble: A Fusion of CNN, Vision Transformer, and Hybrid Models for Multi-Label Chest X-Ray Classification [0.6218519716921521]
We employ deep learning techniques to identify patterns in chest X-rays that correspond to different diseases.
The best individual model was the CoAtNet, which achieved an area under the receiver operating characteristic curve (AUROC) of 84.2%.
arXiv Detail & Related papers (2023-11-13T21:07:07Z) - Instrumental Variable Learning for Chest X-ray Classification [52.68170685918908]
We propose an interpretable instrumental variable (IV) learning framework to eliminate the spurious association and obtain accurate causal representation.
Our approach's performance is demonstrated using the MIMIC-CXR, NIH ChestX-ray 14, and CheXpert datasets.
arXiv Detail & Related papers (2023-05-20T03:12:23Z) - AI can evolve without labels: self-evolving vision transformer for chest
X-ray diagnosis through knowledge distillation [30.075714642990768]
We present a novel deep learning framework that uses knowledge distillation through self-supervised learning and self-training.
Experimental results show that the proposed framework maintains impressive robustness against a real-world environment.
The proposed framework has a great potential for medical imaging, where plenty of data is accumulated every year.
arXiv Detail & Related papers (2022-02-13T22:40:46Z) - The pitfalls of using open data to develop deep learning solutions for
COVID-19 detection in chest X-rays [64.02097860085202]
Deep learning models have been developed to identify COVID-19 from chest X-rays.
Results have been exceptional when training and testing on open-source data.
Data analysis and model evaluations show that the popular open-source dataset COVIDx is not representative of the real clinical problem.
arXiv Detail & Related papers (2021-09-14T10:59:11Z) - Covid-19 Detection from Chest X-ray and Patient Metadata using Graph
Convolutional Neural Networks [6.420262246029286]
We propose a novel Graph Convolution Neural Network (GCN) that is capable of identifying bio-markers of Covid-19 pneumonia.
The proposed method exploits important relational knowledge between data instances and their features using graph representation and applies convolution to learn the graph data.
arXiv Detail & Related papers (2021-05-20T13:13:29Z) - Variational Knowledge Distillation for Disease Classification in Chest
X-Rays [102.04931207504173]
We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays.
We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
arXiv Detail & Related papers (2021-03-19T14:13:56Z) - Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for
Thoracic Disease Identification [83.6017225363714]
deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance.
For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming.
In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
arXiv Detail & Related papers (2021-02-26T02:29:30Z) - Learning Invariant Feature Representation to Improve Generalization
across Chest X-ray Datasets [55.06983249986729]
We show that a deep learning model performing well when tested on the same dataset as training data starts to perform poorly when it is tested on a dataset from a different source.
By employing an adversarial training strategy, we show that a network can be forced to learn a source-invariant representation.
arXiv Detail & Related papers (2020-08-04T07:41:15Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.