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CA-Net: Leveraging Contextual Features for Lung Cancer Prediction

Part of the Lecture Notes in Computer Science book series (LNIP,volume 12905)


In the early diagnosis of lung cancer, an important step is classifying malignancy/benignity for each lung nodule. For this classification, the nodule’s features (e.g., shape, margin) have traditionally been the main focus. Recently, the contextual features attract increasing attention, due to the complementary information they provide. Clinically, such contextual features refer to the features of nodule’s surrounding structures, such that (together with nodule’s features) they can expose discriminate patterns for the malignant/benign, such as vascular convergence and fissural attachment. To leverage such contextual features, we propose a Context Attention Network (CA-Net) which extracts both nodule’s and contextual features and then effectively fuses them during malignancy/benignity classification. To accurately identify the contextual features that contain structures distorted/attached by the nodule, we take the nodule’s features as a reference via an attention mechanism. Further, we propose a feature fusion module that can adaptively adjust the weights of nodule’s and contextual features across nodules. The utility of our proposed method is demonstrated by a noticeable margin over the 1st place on Data Science Bowl 2017 dataset in Kaggle’s competition.


  • Lung cancer prediction
  • Context
  • Domain knowledge

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    The \(a \circ b\) denotes the element-wise product between ab.

  2. 2.

    LIDC-IDRI [12] is not considered for the lack of biopsy confirmed lung cancer labels.

  3. 3.

    Official evaluation metric of the competition, lower value means better performance.


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This work was supported by MOST-2018AAA0102004, NSFC-61625201, and the Beijing Municipal Science and Technology Planning Project (Grant No. Z201100005620008).

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Correspondence to Xinwei Sun .

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Liu, M., Zhang, F., Sun, X., Yu, Y., Wang, Y. (2021). CA-Net: Leveraging Contextual Features for Lung Cancer Prediction. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12905. Springer, Cham.

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