Computer Aided Diagnosis Using Multilevel Image Features on Large-Scale Evaluation

Lü, Le; Devarakota, Pandu; Vikal, Siddharth; Wu, Dijia; Zheng, Yefeng; Wolf, Matthias

doi:10.1007/978-3-319-14104-6_16

Cited by 15 publications

(17 citation statements)

References 23 publications

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“…Previous studies have analyzed three-dimensional patch creation for LN detection [23] , [24] , atlas creation from chest CT [25] and the extraction of multi-level image features [26] , [27] . At present, there are several extensions or variations of the decompositional view representation introduced in [22] , [28] , such as: using a novel vessel-aligned multi-planar image representation for pulmonary embolism detection [29] , fusing unregistered multiview for mammogram analysis [16] and classifying pulmonary peri-fissural nodules via an ensemble of 2D views [12] .…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

Shin

Roth

Gao³

et al. 2016

IEEE Trans. Med. Imaging

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4,822

2,335

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Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets (i.e. ImageNet) and the revival of deep convolutional neural networks (CNN). CNNs enable learning data-driven, highly representative, layered hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pre-trained CNN features, and conducting unsupervised CNN pre-training with supervised fine-tuning. Another effective method is transfer learning, i.e., fine-tuning CNN models (supervised) pre-trained from natural image dataset to medical image tasks (although domain transfer between two medical image datasets is also possible). In this paper, we exploit three important, but previously understudied factors of employing deep convolutional neural networks to computer-aided detection problems. We first explore and evaluate different CNN architectures. The studied models contain 5 thousand to 160 million parameters, and vary in numbers of layers. We then evaluate the influence of dataset scale and spatial image context on performance. Finally, we examine when and why transfer learning from pre-trained ImageNet (via fine-tuning) can be useful. We study two specific computeraided detection (CADe) problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. We achieve the state-of-the-art performance on the mediastinal LN detection, with 85% sensitivity at 3 false positive per patient, and report the first five-fold cross-validation classification results on predicting axial CT slices with ILD categories. Our extensive empirical evaluation, CNN model analysis and valuable insights can be extended to the design of high performance CAD systems for other medical imaging tasks.

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Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

Shin

Roth

Gao³

et al. 2016

IEEE Trans. Med. Imaging

Self Cite

4,822

2,335

View full text Add to dashboard Cite

show abstract

“…Furthermore, segmentation of targeted nodule is the primary step in the machine learning method, and to perform this step, related features are essentially extracted for voxel classification [23,[34][35][36]. Lu et al [37] presented a set of features with translational and rotational invariance to carry out classification. According to research work given by Wu et al [38], it can be observed that their proposed method was based on conditional random fields and features related to nodular shape and texture were used.…”

Section: Related Workmentioning

confidence: 99%

An Efficient DA-Net Architecture for Lung Nodule Segmentation

et al. 2021

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A typical growth of cells inside tissue is normally known as a nodular entity. Lung nodule segmentation from computed tomography (CT) images becomes crucial for early lung cancer diagnosis. An issue that pertains to the segmentation of lung nodules is homogenous modular variants. The resemblance among nodules as well as among neighboring regions is very challenging to deal with. Here, we propose an end-to-end U-Net-based segmentation framework named DA-Net for efficient lung nodule segmentation. This method extracts rich features by integrating compactly and densely linked rich convolutional blocks merged with Atrous convolutions blocks to broaden the view of filters without dropping loss and coverage data. We first extract the lung’s ROI images from the whole CT scan slices using standard image processing operations and k-means clustering. This reduces the search space of the model to only lungs where the nodules are present instead of the whole CT scan slice. The evaluation of the suggested model was performed through utilizing the LIDC-IDRI dataset. According to the results, we found that DA-Net showed good performance, achieving an 81% Dice score value and 71.6% IOU score.

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“…(11), of the divided patches, and choosing the class with the highest probability. While we expect incorporating spatial relationships (Song et al, 2014a) or high-level feature descriptions (Lu et al, 2011(Lu et al, , 2014 would help to improve the classification accuracy, in this study, we used the simple majority voting to focus our method design on the LSRE model. Table 7 shows the classification results at the ROI-level.…”

Section: Evaluation Of Roi Classificationmentioning

confidence: 99%

Locality-constrained Subcluster Representation Ensemble for lung image classification

Song

Cai

Huang

et al. 2015

Medical Image Analysis

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In this paper, we propose a new Locality-constrained Subcluster Representation Ensemble (LSRE) model, to classify high-resolution computed tomography (HRCT) images of interstitial lung diseases (ILDs). Medical images normally exhibit large intra-class variation and inter-class ambiguity in the feature space. Modelling of feature space separation between different classes is thus problematic and this affects the classification performance. Our LSRE model tackles this issue in an ensemble classification construct. The image set is first partitioned into subclusters based on spectral clustering with approximation-based affinity matrix. Basis representations of the test image are then generated with sparse approximation from the subclusters. These basis representations are finally fused with approximation- and distribution-based weights to classify the test image. Our experimental results on a large HRCT database show good performance improvement over existing popular classifiers.

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Computer Aided Diagnosis Using Multilevel Image Features on Large-Scale Evaluation

Cited by 15 publications

References 23 publications

Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

An Efficient DA-Net Architecture for Lung Nodule Segmentation

Locality-constrained Subcluster Representation Ensemble for lung image classification

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