1,057 research outputs found
Self-Guided Multiple Instance Learning for Weakly Supervised Disease Classification and Localization in Chest Radiographs
The lack of fine-grained annotations hinders the deployment of automated
diagnosis systems, which require human-interpretable justification for their
decision process. In this paper, we address the problem of weakly supervised
identification and localization of abnormalities in chest radiographs. To that
end, we introduce a novel loss function for training convolutional neural
networks increasing the \emph{localization confidence} and assisting the
overall \emph{disease identification}. The loss leverages both image- and
patch-level predictions to generate auxiliary supervision. Rather than forming
strictly binary from the predictions as done in previous loss formulations, we
create targets in a more customized manner, which allows the loss to account
for possible misclassification. We show that the supervision provided within
the proposed learning scheme leads to better performance and more precise
predictions on prevalent datasets for multiple-instance learning as well as on
the NIH~ChestX-Ray14 benchmark for disease recognition than previously used
losses
Deep Reinforcement Learning Framework for Thoracic Diseases Classification via Prior Knowledge Guidance
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}
Self-Guided Multiple Instance Learning for Weakly Supervised Thoracic DiseaseClassification and Localizationin Chest Radiographs
Due to the high complexity of medical images and the scarcity of trained personnel, most large-scale radiological datasets are lacking fine-grained annotations and are often only described on image-level. These shortcomings hinder the deployment of automated diagnosis systems, which require human-interpretable justification for their decision process. In this paper, we address the problem of weakly supervised identification and localization of abnormalities in chest radiographs in a multiple-instance learning setting. To that end, we introduce a novel loss function for training convolutional neural networks increasing the localization confidence and assisting the overall disease identification. The loss leverages both image-and patch-level predictions to generate auxiliary supervision and enables specific training at patch-level. Rather than forming strictly binary from the predictions as done in previous loss formulations, we create targets in a more customized manner. This way, the loss accounts for possible misclassification of less certain instances. We show that the supervision provided within the proposed learning scheme leads to better performance and more precise predictions on prevalent datasets for multiple-instance learning as well as on the NIH ChestX-Ray14 benchmark for disease recognition than previously used losses
DualAttNet: Synergistic fusion of image-level and fine-grained disease attention for multi-label lesion detection in chest X-rays
Chest radiographs are the most commonly performed radiological examinations for lesion detection. Recent advances in deep learning have led to encouraging results in various thoracic disease detection tasks. Particularly, the architecture with feature pyramid network performs the ability to recognise targets with different sizes. However, such networks are difficult to focus on lesion regions in chest X-rays due to their high resemblance in vision. In this paper, we propose a dual attention supervised module for multi-label lesion detection in chest radiographs, named DualAttNet. It efficiently fuses global and local lesion classification information based on an image-level attention block and a fine-grained disease attention algorithm. A binary cross entropy loss function is used to calculate the difference between the attention map and ground truth at image level. The generated gradient flow is leveraged to refine pyramid representations and highlight lesionrelated features. We evaluate the proposed model on VinDr-CXR, ChestX-ray8 and COVID-19 datasets. The experimental results show that DualAttNet surpasses baselines by 0.6% to 2.7% mAP and 1.4% to 4.7% AP50 with different detection architectures. The code for our work and more technical details can be found at https://github.com/xq141839/DualAttNet
Recommended from our members
Deep learning for cardiac image segmentation: A review
Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research
Data efficient deep learning for medical image analysis: A survey
The rapid evolution of deep learning has significantly advanced the field of
medical image analysis. However, despite these achievements, the further
enhancement of deep learning models for medical image analysis faces a
significant challenge due to the scarcity of large, well-annotated datasets. To
address this issue, recent years have witnessed a growing emphasis on the
development of data-efficient deep learning methods. This paper conducts a
thorough review of data-efficient deep learning methods for medical image
analysis. To this end, we categorize these methods based on the level of
supervision they rely on, encompassing categories such as no supervision,
inexact supervision, incomplete supervision, inaccurate supervision, and only
limited supervision. We further divide these categories into finer
subcategories. For example, we categorize inexact supervision into multiple
instance learning and learning with weak annotations. Similarly, we categorize
incomplete supervision into semi-supervised learning, active learning, and
domain-adaptive learning and so on. Furthermore, we systematically summarize
commonly used datasets for data efficient deep learning in medical image
analysis and investigate future research directions to conclude this survey.Comment: Under Revie
AI in drug discovery and its clinical relevance
The COVID-19 pandemic has emphasized the need for novel drug discovery process. However, the journey from conceptualizing a drug to its eventual implementation in clinical settings is a long, complex, and expensive process, with many potential points of failure. Over the past decade, a vast growth in medical information has coincided with advances in computational hardware (cloud computing, GPUs, and TPUs) and the rise of deep learning. Medical data generated from large molecular screening profiles, personal health or pathology records, and public health organizations could benefit from analysis by Artificial Intelligence (AI) approaches to speed up and prevent failures in the drug discovery pipeline. We present applications of AI at various stages of drug discovery pipelines, including the inherently computational approaches of de novo design and prediction of a drug's likely properties. Open-source databases and AI-based software tools that facilitate drug design are discussed along with their associated problems of molecule representation, data collection, complexity, labeling, and disparities among labels. How contemporary AI methods, such as graph neural networks, reinforcement learning, and generated models, along with structure-based methods, (i.e., molecular dynamics simulations and molecular docking) can contribute to drug discovery applications and analysis of drug responses is also explored. Finally, recent developments and investments in AI-based start-up companies for biotechnology, drug design and their current progress, hopes and promotions are discussed in this article.Â
Other InformationPublished in:HeliyonLicense: https://creativecommons.org/licenses/by/4.0/See article on publisher's website: https://doi.org/10.1016/j.heliyon.2023.e17575Â </p
- …