SANet: A Slice-Aware Network for Pulmonary Nodule Detection

Mei, Jie; Cheng, Ming–Ming; Xu, Gang; Wan, Lan-Ruo; Zhang, Huan

doi:10.1109/tpami.2021.3065086

Cited by 63 publications

(74 citation statements)

References 66 publications

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“…Newer studies have been proposed with relevant results. For example, Zhu et al [28] treated the quality of the images, and Peng et al [35] and Mei et al [36] proposed new DCNN architectures. However, they do not surpass all our results in the different steps (NCD and FPR) with the evaluation metrics average sensitivity at FPs/Scan or CPM.…”

Section: Experiments and Resultsmentioning

confidence: 99%

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Classifier Ensemble Based on Computed Tomography Attenuation Patterns for Computer-Aided Detection System

Pereira¹,

Andrade

Escuissato

et al. 2021

IEEE Access

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Cancer is one of the leading causes of mortality worldwide, specifically lung cancer. Computer-Aided Detection (CADe) systems are being proposed to assist radiologists in the task of pulmonary nodule detection. In this paper, we propose a CADe system that uses Deep Convolutional Neural Network (DCNN). In the Nodule Candidate Detection (NCD) step, we used Mask Region-Convolutional Neural Network (Mask R-CNN) to detect bounding boxes in 2D slices of low-dose Computed Tomography (CT) scans. In the False Positive Reduction (FPR) step, we used a classifier ensemble based on CT attenuation patterns to boost 3D pulmonary nodule classification performance. The final confidence index generated by the CADe system to the pulmonary nodule candidates is the average of the prediction obtained with the NCD and FPR steps. The CADe system was validated on the publicly available LUng Nodule Analysis 2016 (LUNA16) challenge and obtained a sensitivity of 94.90% and an average of 1.0 False Positives per scan (FP/Scan), against 96.90% of the proposal that combines different existing CADe systems. To the best of our knowledge, our proposal has one of the best results of CADe systems, outperforming other state-of-the-art individual methods.

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Section: Experiments and Resultsmentioning

confidence: 99%

“…The CPM score of their scheme is 92.30% on the LUNA16 dataset. In another approach, Mei et al [36] proposed a CADe system based on DCNN named SANet. Initially, they used a 3D RPN to generate pulmonary nodule candidates.…”

Section: Related Workmentioning

confidence: 99%

Classifier Ensemble Based on Computed Tomography Attenuation Patterns for Computer-Aided Detection System

Pereira¹,

Andrade

Escuissato

et al. 2021

IEEE Access

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“…Liao et al [14] utilized a 3D Faster RCNN with a U-net structure, supplementing it with a location crop to aid in determining whether the object is a nodule. Mei et al [10] proposed a slice grouped non-local (SGNL) module for capturing longrange dependencies between any positions in the feature maps. However, these methods treat every pixel in the feature maps the same, which is contrary to how experienced doctors assess the lung areas.…”

Section: A Spatial Context and Attention Mechanismmentioning

confidence: 99%

“…Moreover, we find that commonly used public datasets, such as LIDC-IDRI [12], LUNA 16 [13] and PN9 [10] contain many small nodules, ranging in size from 3mm to 10mm, and are composed of an imbalanced proportion of different sized lung nodules. However, nodules less than 5 mm in diameter, in need of long-term follow-ups, are not a primary focus of therapeutic applications.…”

Section: Introductionmentioning

confidence: 96%

“…They screened nodules using convolutional networks or modified object detection models. To better characterize nodules for detection due to their variety in sizes, shapes and texture, the dual path network [9], slice-grouped non-local module [10], anchor-free framework [11] etc. are developed to improve the performance in support of doctors' diagnoses.…”

Section: Introductionmentioning

confidence: 99%

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MHSnet: Multi-head and Spatial Attention Network with False-Positive Reduction for Pulmonary Nodules Detection

Mai¹,

Wang²,

Zheng³

et al. 2022

Preprint

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The mortality of lung cancer has ranked high among cancers for many years. Early detection of lung cancer is critical for disease prevention, cure, and mortality rate reduction. However, existing detection methods on pulmonary nodules introduce an excessive number of false positive proposals in order to achieve high sensitivity, which is not practical in clinical situations. In this paper, we propose the multi-head detection and spatial squeezeand-attention network, MHSnet, to detect pulmonary nodules, in order to aid doctors in the early diagnosis of lung cancers. Specifically, we first introduce multi-head detectors and skip connections to customize for the variety of nodules in sizes, shapes and types and capture multi-scale features. Then, we implement a spatial attention module to enable the network to focus on different regions differently inspired by how experienced clinicians screen CT images, which results in fewer false positive proposals. Lastly, we present a lightweight but effective false positive reduction module with the Linear Regression model to cut down the number of false positive proposals, without any constraints on the front network. Extensive experimental results compared with the state-of-the-art models have shown the superiority of the MHSnet in terms of the average FROC, sensitivity and especially false discovery rate (2.98% and 2.18% improvement in terms of average FROC and sensitivity, 5.62% and 28.33% decrease in terms of false discovery rate and average candidates per scan). The false positive reduction module significantly decreases the average number of candidates generated per scan by 68.11% and the false discovery rate by 13.48%, which is promising to reduce distracted proposals for the downstream tasks based on the detection results.

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PUNDIT: Pulmonary nodule detection with image category transformation

Zhao

et al. 2023

Medical Physics

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Background: Convolutional neural networks (CNNs) have achieved great success in pulmonary nodules detection, which plays an important role in lung cancer screening. Purpose: In this paper, we proposed a novel strategy for pulmonary nodule detection by learning it from a harder task, which was to transform nodule images into normal images. We named this strategy as pulmonary nodule detection with image category transformation (PUNDIT). Methods: There were two steps for nodules detection, nodule candidate detection and false positive (FP) reduction. In nodule candidate detection step, a segmentation-based framework was built for detection. We designed an image category transformation (ICT) task to translate nodule images into pixel-to-pixel normal images and share the information of detection and transformation tasks by multitask learning. As for references of transformation tasks, we proposed background consistency losses into standard cycle-consistent adversarial networks, which can solve the problem of background uncontrolled changing. A three-dimensional network was used in FP reduction step. Results: PUNDIT was evaluated in two datasets, cancer screening dataset (CSD) with 1186 nodules for cross-validation and (CTD) with 3668 nodules for external test. Results were mainly evaluated by competition performance metric (CPM), the average sensitivity at seven predefined FP rates. The CPM was improved from 0.906 to 0.931 in CSD, and from 0.835 to 0.848 in CTD. Conclusions: Experimental results showed that PUNDIT can improve the performance of pulmonary nodules detection effectively.

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SANet: A Slice-Aware Network for Pulmonary Nodule Detection

Cited by 63 publications

References 66 publications

Classifier Ensemble Based on Computed Tomography Attenuation Patterns for Computer-Aided Detection System

Classifier Ensemble Based on Computed Tomography Attenuation Patterns for Computer-Aided Detection System

MHSnet: Multi-head and Spatial Attention Network with False-Positive Reduction for Pulmonary Nodules Detection

PUNDIT: Pulmonary nodule detection with image category transformation

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