# Joint Learning for Pulmonary Nodule Segmentation, Attributes and Malignancy Prediction

arXiv: https://arxiv.org/abs/1802.03584 · HTML: https://mlsanity.com/paper/1802.03584/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2018.

## Built on by (most cited first, 16 of 16)

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- [3D-Morphomics, Morphological Features on CT scans for lung nodule malignancy diagnosis](https://mlsanity.com/paper/2207.13830/) (2022)
- [Are Deep Learning Classification Results Obtained on CT Scans Fair and Interpretable?](https://mlsanity.com/paper/2309.12632/) (2023)
- [Interpretative Computer-aided Lung Cancer Diagnosis: from Radiology Analysis to Malignancy Evaluation](https://mlsanity.com/paper/2102.10919/) (2021)
- [Attention-Enhanced Cross-Task Network for Analysing Multiple Attributes of Lung Nodules in CT](https://mlsanity.com/paper/2103.03931/) (2021)
- [Faithful learning with sure data for lung nodule diagnosis](https://mlsanity.com/paper/2202.12515/) (2022)
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## Abstract (arXiv)

Refer to the literature of lung nodule classification, many studies adopt Convolutional Neural Networks (CNN) to directly predict the malignancy of lung nodules with original thoracic Computed Tomography (CT) and nodule location. However, these studies cannot tell how the CNN works in terms of predicting the malignancy of the given nodule, e.g., it's hard to conclude that whether the region within the nodule or the contextual information matters according to the output of the CNN. In this paper, we propose an interpretable and multi-task learning CNN -- Joint learning for Pulmonary Nodule Segmentation Attributes and Malignancy Prediction (PN-SAMP). It is able to not only accurately predict the malignancy of lung nodules, but also provide semantic high-level attributes as well as the areas of detected nodules. Moreover, the combination of nodule segmentation, attributes and malignancy prediction is helpful to improve the performance of each single task. In addition, inspired by the fact that radiologists often change window widths and window centers to help to make decision on uncertain nodules, PN-SAMP mixes multiple WW/WC together to gain information for the raw CT input images. To verify the effectiveness of the proposed method, the evaluation is implemented on the public LIDC-IDRI dataset, which is one of the largest dataset for lung nodule malignancy prediction. Experiments indicate that the proposed PN-SAMP achieves significant improvement with respect to lung nodule classification, and promising performance on lung nodule segmentation and attribute learning, compared with the-state-of-the-art methods.