Source-linked AI summary
Diagnostic Classification Of Lung Nodules Using 3D Neural Networks
Raunak Dey, Zhongjie Lu, Yi Hong
TL;DR
The paper tackles benign-versus-malignant lung nodule classification from 3D CT images, a difficult task for radiologic diagnosis. It proposes four two-pathway 3D networks and evaluates them on public and private datasets. MoDenseNet achieves the strongest reported performance on LIDC-IDRI and remains best among the proposed networks on the private dataset, with transfer learning used for the smaller dataset.
Problem
The paper addresses difficult benign-versus-malignant pulmonary nodule diagnosis from CT images, where subtle differences challenge radiologists and motivate objective CAD assistance.
Method
The paper proposes four two-pathway 3D neural networks, including a basic 3D CNN, multi-output network, 3D DenseNet, and their combination, with transfer learning for the smaller dataset.
Results
90.40% accuracy was achieved by MoDenseNet on LIDC-IDRI in cross-validation, and it performed best among the four proposed networks on the private dataset.
Takeaways & Limitations
MoDenseNet provides a strong end-to-end 3D approach for lung nodule classification across the evaluated datasets, while LIDC-IDRI pretraining supports use on a smaller dataset.
Takeaways & Limitations
The study still requires manual annotations for nodule locations; automatic pulmonary nodule detection is identified as future work.
Abstract
from arXiv · showhide
Lung cancer is the leading cause of cancer-related death worldwide. Early diagnosis of pulmonary nodules in Computed Tomography (CT) chest scans provides an opportunity for designing effective treatment and making financial and care plans. In this paper, we consider the problem of diagnostic classification between benign and malignant lung nodules in CT images, which aims to learn a direct mapping from 3D images to class labels. To achieve this goal, four two-pathway Convolutional Neural Networks (CNN) are proposed, including a basic 3D CNN, a novel multi-output network, a 3D DenseNet, and an augmented 3D DenseNet with multi-outputs. These four networks are evaluated on the public LIDC-IDRI dataset and outperform most existing methods. In particular, the 3D multi-output DenseNet (MoDenseNet) achieves the state-of-the-art classification accuracy on the task of end-to-end lung nodule diagnosis. In addition, the networks pretrained on the LIDC-IDRI dataset can be further extended to handle smaller datasets using transfer learning. This is demonstrated on our dataset with encouraging prediction accuracy in lung nodule classification.
1. INTRODUCTION
The paper addresses difficult benign-versus-malignant lung nodule diagnosis by proposing end-to-end 3D neural networks for CT images. On LIDC-IDRI, MoDenseNet achieves the best reported performance and outperforms a recent model.
- 1. INTRODUCTION: Clinical diagnosis is difficult because benign and malignant nodules have subtle differences, while radiologic accuracy varies with clinician experience.CAD is presented as a non-invasive approach for objective assistance.
- 1. INTRODUCTION: Existing CAD methods use either hand-crafted radiological features or deep networks that automatically learn features from CT images.Hand-crafted approaches measure traits such as size, location, shape, and texture, but feature selection can introduce measurement errors.
- 1. INTRODUCTION: Four end-to-end 3D networks classify pulmonary nodules as benign or malignant using two-pathway inputs, intermediate outputs, dense blocks, or their combination.The proposed variants are a basic 3D CNN, a multi-output network, a 3D DenseNet, and a combined multi-output DenseNet.
- 1. INTRODUCTION: 90.40% accuracy was achieved by 3D multi-output DenseNet on LIDC-IDRI in cross-validation, exceeding a recent pulmonary nodule classification model in accuracy and ROC area.The comparison is reported for the LIDC-IDRI dataset.
- 1. INTRODUCTION: MoDenseNet achieves the authors’ state-of-the-art LIDC-IDRI result using an end-to-end model with 3D CT images of uniform slice thickness.The paper also reports that MoDenseNet performs best among the four proposed networks on the private dataset.
2. METHODS
The methods use localized 3D CT patches and two-pathway neural architectures to classify lung nodules. The study includes multi-output and dense-connection variants and applies transfer learning when the target dataset is small.
- 2. METHODS: The networks classify pulmonary nodules from 3D CT scans using the nodule location, rather than relying on single- or multi-view 2D images.This design is motivated by radiologists’ use of multiple slices and 3D nodule information.
- 2. METHODS: Small, location-centered 3D regions are used because whole CT scans are impractical for deep networks and many nodules occupy fewer than 10 slices.The networks use two pathways that accept 3D image patches at different scales.
- 2. METHODS: The basic 3D CNN uses two pathways with different-scale inputs to capture local nodule detail and global surrounding tissue.One pathway receives a 50 × 50 × 5 volume and the other receives a 100 × 100 × 10 volume.
- 2. METHODS: The multi-output network adds intermediate outputs and connects feature maps before those outputs and from final convolutional layers to the classifier.These early outputs provide immediate feedback from intermediate error evaluations.
- 2. METHODS: Transfer learning freezes convolutional layers pretrained on LIDC-IDRI and retrains the final layer on the 147-sample private dataset.The private dataset is substantially smaller than LIDC-IDRI, which has 686 available lung nodule samples for experiments.
3. EXPERIMENTS
The proposed networks were evaluated on LIDC-IDRI and the authors’ dataset, where MoDenseNet achieved the strongest reported performance and transfer learning improved results on the smaller dataset.
- The LIDC-IDRI experiments used 1010 CT scans annotated by four radiologists, with five-fold cross-validation and weighted binary cross-entropy for the imbalanced binary task.
- 90.40% accuracy and 0.9548 AUC were achieved by the multi-output DenseNet on LIDC-IDRI.The four proposed networks contained 28 to 34.8 million parameters.
- Transfer learning improved AUC and classification accuracy for all four networks on the authors’ dataset.The dataset retained the ranking observed on LIDC-IDRI, with MoDenseNet performing best.
4. DISCUSSION
The discussion concludes that direct 3D processing and shorter input-to-output paths support lung-nodule classification, while future work targets interpretability and automated nodule detection.
- Directly processing 3D volumes performed better than using 2D slices or multi-view approximations when slice thickness was consistent.
- Connections between layers and early outputs provide prompt error back-propagation that improves network optimization.
- Future work will examine whether learned features are consistent with radiologists’ diagnostic features and automate pulmonary nodule detection.Automated detection would relax the requirement for manually annotated nodule locations.