Shape and margin-aware lung nodule classification in low-dose CT images via soft activation mapping

Lei, Yiming; Tian, Yukun; Shan, Hongming; Zhang, Junping; Wang, Ge; Kalra, Mannudeep K.

doi:10.1016/j.media.2019.101628

Cited by 65 publications

(40 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the first convolutional layer can extract diverse representations through multiple slices, this advantage can be weakened with the increased depth of the model. In other words, deeper networks may not perform better than shallower networks because of the limited data set [35].…”

Section: Principal Findingsmentioning

confidence: 99%

Deep Learning Models for Predicting Severe Progression in COVID-19-Infected Patients: Retrospective Study

Ho¹,

Park²,

Kim³

et al. 2021

JMIR Med Inform

View full text Add to dashboard Cite

Background Many COVID-19 patients rapidly progress to respiratory failure with a broad range of severities. Identification of high-risk cases is critical for early intervention. Objective The aim of this study is to develop deep learning models that can rapidly identify high-risk COVID-19 patients based on computed tomography (CT) images and clinical data. Methods We analyzed 297 COVID-19 patients from five hospitals in Daegu, South Korea. A mixed artificial convolutional neural network (ACNN) model, combining an artificial neural network for clinical data and a convolutional neural network for 3D CT imaging data, was developed to classify these cases as either high risk of severe progression (ie, event) or low risk (ie, event-free). Results Using the mixed ACNN model, we were able to obtain high classification performance using novel coronavirus pneumonia lesion images (ie, 93.9% accuracy, 80.8% sensitivity, 96.9% specificity, and 0.916 area under the curve [AUC] score) and lung segmentation images (ie, 94.3% accuracy, 74.7% sensitivity, 95.9% specificity, and 0.928 AUC score) for event versus event-free groups. Conclusions Our study successfully differentiated high-risk cases among COVID-19 patients using imaging and clinical features. The developed model can be used as a predictive tool for interventions in aggressive therapies.

show abstract

Section: Principal Findingsmentioning

confidence: 99%

Deep Learning Models for Predicting Severe Progression in COVID-19-Infected Patients: Retrospective Study

Ho¹,

Park²,

Kim³

et al. 2021

JMIR Med Inform

View full text Add to dashboard Cite

show abstract

“…Deep learning methods are also widely used in medical image analysis ( Chen et al, 2019 ; Huang et al, 2020b ; Lei et al, 2020 ; Li et al, 2020b ; Litjens et al, 2017 ; Shen et al, 2017 ). Recently, deep learning has been utilized in COVID-19 diagnosis and evaluation, and the results have been encouraging ( Shi et al, 2020a ).…”

Section: Introductionmentioning

confidence: 99%

Dual-branch combination network (DCN): Towards accurate diagnosis and lesion segmentation of COVID-19 using CT images

Gao

Jiang

et al. 2021

Medical Image Analysis

146

View full text Add to dashboard Cite

Highlights We develop a dual-branch combination network (DCN) for combined segmentation and classification of COVID-19 using CT images. Inspired by the attention mechanism, we propose a lesion attention (LA) module to improve the sensitivity to CT images with small lesions and facilitate early screening of COVID-19. The LA module provide accurate attention maps to improve the interpretability of the network and contribute to further assessment of the classification result.

show abstract

“…Although the 3D CNNs yielded promising results, learning a large number of hyperparameters associated with 3D networks from a limited number of medical images remains a challenge. As an alternative, accurate nodule classification results have also been achieved using patch-based multi-view slices to train 2.5D CNNs [5,[27][28][29]. Along with powerful end-to-end classification networks, another family of approaches utilizes deep features extracted from unsupervised Auto-Encoder (AE)-like reconstruction networks followed by a supervised learning algorithm [30][31][32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, a large number of previous studies have investigated the performance of their proposed models on the publicly available LIDC-IDRI dataset [39]. As accurate classification results have already been achieved on that dataset [29,38], it is necessary to provide another challenging large-scale dataset for external validation.…”

Section: Introductionmentioning

confidence: 99%

Benign-malignant pulmonary nodule classification in low-dose CT with convolutional features

et al. 2021

View full text Add to dashboard Cite

Low-Dose Computed Tomography (LDCT) is the most common imaging modality for lung cancer diagnosis. The presence of nodules in the scans does not necessarily portend lung cancer, as there is an intricate relationship between nodule characteristics and lung cancer. Therefore, benign-malignant pulmonary nodule classification at early detection is a crucial step to improve diagnosis and prolong patient survival. The aim of this study is to propose a method for predicting nodule malignancy based on deep abstract features. Methods: To efficiently capture both intra-nodule heterogeneities and contextual information of the pulmonary nodules, a dual pathway model was developed to integrate the intra-nodule characteristics with contextual attributes. The proposed approach was implemented with both supervised and unsupervised learning schemes. A random forest model was added as a second component on top of the networks to generate the classification results. The discrimination power of the model was evaluated by calculating the Area Under the Receiver Operating Characteristic Curve (AUROC) metric. Results: Experiments on 1297 manually segmented nodules show that the integration of context and target supervised deep features have a great potential for accurate prediction, resulting in a discrimination power of 0.936 in terms of AUROC, which outperformed the classification performance of the Kaggle 2017 challenge winner. Conclusion:Empirical results demonstrate that integrating nodule target and context images into a unified network improves the discrimination power, outperforming the conventional single pathway convolutional neural networks.

show abstract

Shape and margin-aware lung nodule classification in low-dose CT images via soft activation mapping

Cited by 65 publications

References 26 publications

Deep Learning Models for Predicting Severe Progression in COVID-19-Infected Patients: Retrospective Study

Deep Learning Models for Predicting Severe Progression in COVID-19-Infected Patients: Retrospective Study

Dual-branch combination network (DCN): Towards accurate diagnosis and lesion segmentation of COVID-19 using CT images

Benign-malignant pulmonary nodule classification in low-dose CT with convolutional features

Contact Info

Product

Resources

About