# ProCAN: Progressive Growing Channel Attentive Non-Local Network for Lung Nodule Classification

arXiv: https://arxiv.org/abs/2010.15417 · HTML: https://mlsanity.com/paper/2010.15417/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2020.

## Built on by (most cited first, 9 of 9)

- [Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review](https://mlsanity.com/paper/2301.03505/) (2023)
- [Learning from Ambiguous Labels for Lung Nodule Malignancy Prediction](https://mlsanity.com/paper/2104.11436/) (2021)
- [A survey on attention mechanisms for medical applications: are we moving towards better algorithms?](https://mlsanity.com/paper/2204.12406/) (2022)
- [Is attention all you need in medical image analysis? A review](https://mlsanity.com/paper/2307.12775/) (2023)
- [Unsupervised Contrastive Learning based Transformer for Lung Nodule Detection](https://mlsanity.com/paper/2205.00122/) (2022)
- [RADIFUSION: A multi-radiomics deep learning based breast cancer risk prediction model using sequential mammographic images with image attention and bilateral asymmetry refinement](https://mlsanity.com/paper/2304.00257/) (2023)
- [Integration of Radiomics and Tumor Biomarkers in Interpretable Machine Learning Models](https://mlsanity.com/paper/2303.11177/) (2023)
- [3D-Morphomics, Morphological Features on CT scans for lung nodule malignancy diagnosis](https://mlsanity.com/paper/2207.13830/) (2022)
- [Deep fusion of gray level co-occurrence matrices for lung nodule classification](https://mlsanity.com/paper/2205.05123/) (2022)

## Abstract (arXiv)

Lung cancer classification in screening computed tomography (CT) scans is one of the most crucial tasks for early detection of this disease. Many lives can be saved if we are able to accurately classify malignant/cancerous lung nodules. Consequently, several deep learning based models have been proposed recently to classify lung nodules as malignant or benign. Nevertheless, the large variation in the size and heterogeneous appearance of the nodules makes this task an extremely challenging one. We propose a new Progressive Growing Channel Attentive Non-Local (ProCAN) network for lung nodule classification. The proposed method addresses this challenge from three different aspects. First, we enrich the Non-Local network by adding channel-wise attention capability to it. Second, we apply Curriculum Learning principles, whereby we first train our model on easy examples before hard ones. Third, as the classification task gets harder during the Curriculum learning, our model is progressively grown to increase its capability of handling the task at hand. We examined our proposed method on two different public datasets and compared its performance with state-of-the-art methods in the literature. The results show that the ProCAN model outperforms state-of-the-art methods and achieves an AUC of 98.05% and an accuracy of 95.28% on the LIDC-IDRI dataset. Moreover, we conducted extensive ablation studies to analyze the contribution and effects of each new component of our proposed method.