Multi-Label Chest X-Ray Image Classification via Semantic Similarity Graph Embedding

Chen, Bingzhi; Zhang, Zheng; Li, Yingjian; Lu, Yao; Zhang, David

doi:10.1109/tcsvt.2021.3079900

Cited by 34 publications

(24 citation statements)

References 37 publications

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“…Chen et al [24] proposed a label co-occurrence learning framework based on GNN and CNN models to explore the correlations between pathological features. To enable the model to leverage prior knowledge in diagnosing thoracic diseases like a professional radiologist, Chen et al [21] introduced a Semantic Similarity Graph Embedding (SSGE) module designed to investigate the semantic similarity between images and optimize the feature extraction process. Lee et al [23] proposed a hybrid deep learning model (CheXGAT) based on CNN and graph convolution neural networks (GNN), which uses self-attention to aggregate domain features from graphical structures to enhance potential correlations between thoracic diseases.…”

Section: Related Workmentioning

confidence: 99%

Multi-Level Residual Feature Fusion Network for Thoracic Disease Classification in Chest X-Ray Images

LAI

Adamu

et al. 2023

IEEE Access

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Motivation: Automated identification of thoracic diseases from chest X-ray images (CXR) is a significant area in computer-aided diagnosis. However, most existing methods have limited ability to extract multi-scale features and accurately capture the spatial location of lesions when dealing with thoracic diseases that exhibit concurrency and large variations in lesion size. Method: Based on the above problems, we propose a multi-level residual feature fusion network (MLRFNet) for classifying thoracic diseases. Our approach can quickly capture receptive field information across different lesion sizes and enhance disease-specific features within the spatial domain on feature maps. The MLRFNet comprises two main components: a feature extractor that learns multi-scale semantic information from chest X-ray images and a multi-level residual feature classifier (MRFC) that refines disease-specific pathological features at spatial locations to reduce interference from irrelevant regions. Additionally, the ECA attention modules connect both components to enable flexible channel-wise focus on critical pathological information. Results: We evaluated the performance of MLRFNet through a series of experiments on two datasets: ChestX-Ray14 and CheXpert. Our results show that MLRFNet achieves an average AUC of 0.853 on the ChestX-Ray14 dataset and 0.904 on the CheXpert dataset. Conclusion: The results of experiments demonstrate that our proposed method works better than the current state-of-the-art baselines. Future work will focus on investigating the interdependencies among labels for thoracic diseases and techniques for model compression.INDEX TERMS Chest X-ray image classification, convolutional neural network, attention mechanism, residual feature vector, medical image processing.

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Section: Related Workmentioning

confidence: 99%

Multi-Level Residual Feature Fusion Network for Thoracic Disease Classification in Chest X-Ray Images

LAI

Adamu

et al. 2023

IEEE Access

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“…Exploring image similarities and optimizing visual feature embedding for multi-label chest X-ray image classification was suggested using a graph convolutional network-based semantic similarity graph embedding framework [17]. Multiple label categorizations have long been investigated in semi-supervised learning in order to reduce human tagging effort.…”

Section: Related Workmentioning

confidence: 99%

Improved Multi-Label Image Classification Performance using Supervised CNN-LSTM Deep Neural Network

S¹,

Lakshmi²

2022

INDJCSE

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Deep Convolutional Neural Network (CNN) classification of single-object image labels has shown high efficiency. However, the great bulk of actual application data comprises of multiple label object images that belong to a variety of scenes, objects, and actions in a single image. Most of the recent research studies on multiple object label classification rely on individual classifiers for each label category and use probability ranking for final classification. These methods already in place work better, but they cannot find the dependencies between multiple labels in an image. In this paper, we use deep CNN architecture and long short-term memory (LSTM) to solve the problem of label dependence. Our proposed CNN-LSTM methodology learns the embeddings of object label to depict semantic object label dependence and image label association using a robust multi-label classifier cost function (RMLC), which is a ramp loss function.The feature extraction is carried out by a convolution neural network (CNN) pipeline; whereas multi-object label correlation is identified by LSTM using object labels and features extracted from input images. We use the loss function to make sure that correlated labels and corresponding features map close to each other, limiting the high value updation of weights for the images with improper labels, and the object label prediction progresses every time, which helps to improve the multi-label learning task. Experiments conducted using the proposed framework on benchmark visual recognition datasets such as CIFAR-10, STL-10, PASCAL VOC 2007, MS-COCO, and NUS-WIDE provided performance comparatively better than many existing methods in terms of accuracy and mean average precision. The CNN-LSTM + RMLC achieve the best test accuracy of 95.56 % on the STL dataset, which is 4% higher than the existing method, and the best mean average precision (mAP) of 82.6 on the MS-COCO dataset, which shows the feasibility and usefulness of our suggested framework on multiple label image classification.

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“…KD has recently been used in several CXR image classification works. For instance, numerous works [23], [24], [25], [26] can be seen in CXR image classification domain. Furthermore, XAI is a new machine learning research topic that aims to unbox how AI systems make black-box decisions.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, XAI has gained popularity and attracted significant interest from the medical image analysis research community, as it tackles two essential issues: transparency and accountability [28]. CXR image classification [10], [23], [24], [25] utilize XAI for explaining their models to boost the confidence. The preceding research articles use visualization-based XAI techniques to give visual explanations for their models.…”

Section: Introductionmentioning

confidence: 99%

Explainable Knowledge Distillation for On-Device Chest X-Ray Classification

Termritthikun

Umer

Suwanwimolkul³

et al. 2024

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Automated multi-label chest X-rays (CXR) image classification has achieved substantial progress in clinical diagnosis via utilizing sophisticated deep learning approaches. However, most deep models have high computational demands, which makes them less feasible for compact devices with low computational requirements. To overcome this problem, we propose a knowledge distillation (KD) strategy to create the compact deep learning model for the real-time multi-label CXR image classification. We study different alternatives of CNNs and Transforms as the teacher to distill the knowledge to a smaller student. Then, we employed explainable artificial intelligence (XAI) to provide the visual explanation for the model decision improved by the KD. Our results on three benchmark CXR datasets show that our KD strategy provides the improved performance on the compact student model, thus being the feasible choice for many limited hardware platforms. For instance, when using DenseNet161 as the teacher network, EEEA-Net-C2 achieved an AUC of 83.7%, 87.1%, and 88.7% on the ChestX-ray14, CheXpert, and PadChest datasets, respectively, with fewer parameters of 4.7 million and computational cost of 0.3 billion FLOPS.

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Multi-Label Chest X-Ray Image Classification via Semantic Similarity Graph Embedding

Cited by 34 publications

References 37 publications

Multi-Level Residual Feature Fusion Network for Thoracic Disease Classification in Chest X-Ray Images

Multi-Level Residual Feature Fusion Network for Thoracic Disease Classification in Chest X-Ray Images

Improved Multi-Label Image Classification Performance using Supervised CNN-LSTM Deep Neural Network

Explainable Knowledge Distillation for On-Device Chest X-Ray Classification

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