# Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network

arXiv: https://arxiv.org/abs/1711.08324 · HTML: https://mlsanity.com/paper/1711.08324/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2017.

## Built on by (most cited first, 30 of 53)

- [Transformers in Medical Imaging: A Survey](https://mlsanity.com/paper/2201.09873/) (2022)
- [Recent advances and clinical applications of deep learning in medical image analysis](https://mlsanity.com/paper/2105.13381/) (2021)
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- [SSMD: Semi-Supervised Medical Image Detection with Adaptive Consistency and Heterogeneous Perturbation](https://mlsanity.com/paper/2106.01544/) (2021)
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- [A Unified Framework for Generalized Low-Shot Medical Image Segmentation with Scarce Data](https://mlsanity.com/paper/2110.09260/) (2021)
- [SCPM-Net: An Anchor-free 3D Lung Nodule Detection Network using Sphere Representation and Center Points Matching](https://mlsanity.com/paper/2104.05215/) (2021)
- [M3Dsynth: A dataset of medical 3D images with AI-generated local manipulations](https://mlsanity.com/paper/2309.07973/) (2023)
- [Stabilized Medical Image Attacks](https://mlsanity.com/paper/2103.05232/) (2021)
- [Meningioma segmentation in T1-weighted MRI leveraging global context and attention mechanisms](https://mlsanity.com/paper/2101.07715/) (2021)
- [Time-distance vision transformers in lung cancer diagnosis from longitudinal computed tomography](https://mlsanity.com/paper/2209.01676/) (2022)
- [Attention Mechanisms in Medical Image Segmentation: A Survey](https://mlsanity.com/paper/2305.17937/) (2023)
- [Longitudinal Multimodal Transformer Integrating Imaging and Latent Clinical Signatures From Routine EHRs for Pulmonary Nodule Classification](https://mlsanity.com/paper/2304.02836/) (2023)
- [DeepMMSA: A Novel Multimodal Deep Learning Method for Non-small Cell Lung Cancer Survival Analysis](https://mlsanity.com/paper/2106.06744/) (2021)
- [Automated Pulmonary Embolism Detection from CTPA Images Using an End-to-End Convolutional Neural Network](https://mlsanity.com/paper/2111.05506/) (2021)
- [Conditional Training with Bounding Map for Universal Lesion Detection](https://mlsanity.com/paper/2103.12277/) (2021)
- [Lung Cancer Risk Estimation with Incomplete Data: A Joint Missing Imputation Perspective](https://mlsanity.com/paper/2107.11882/) (2021)
- [Machine Learning based Medical Image Deepfake Detection: A Comparative Study](https://mlsanity.com/paper/2109.12800/) (2021)
- [A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study](https://mlsanity.com/paper/2303.10789/) (2023)
- [Medical Imaging with Deep Learning for COVID- 19 Diagnosis: A Comprehensive Review](https://mlsanity.com/paper/2107.09602/) (2021)
- [Enhancing Cancer Prediction in Challenging Screen-Detected Incident Lung Nodules Using Time-Series Deep Learning](https://mlsanity.com/paper/2203.16606/) (2022)
- [LSSANet: A Long Short Slice-Aware Network for Pulmonary Nodule Detection](https://mlsanity.com/paper/2208.02122/) (2022)
- [A Comparative Study of Confidence Calibration in Deep Learning: From Computer Vision to Medical Imaging](https://mlsanity.com/paper/2206.08833/) (2022)
- [SGDA: Towards 3D Universal Pulmonary Nodule Detection via Slice Grouped Domain Attention](https://mlsanity.com/paper/2303.03625/) (2023)
- [Introduction to Medical Imaging Informatics](https://mlsanity.com/paper/2306.00421/) (2023)
- [Technical Report: Quality Assessment Tool for Machine Learning with Clinical CT](https://mlsanity.com/paper/2107.12842/) (2021)
- [Segmentation-Consistent Probabilistic Lesion Counting](https://mlsanity.com/paper/2204.05276/) (2022)
- [Parse and Recall: Towards Accurate Lung Nodule Malignancy Prediction like Radiologists](https://mlsanity.com/paper/2307.10824/) (2023)

## Abstract (arXiv)

Automatic diagnosing lung cancer from Computed Tomography (CT) scans involves two steps: detect all suspicious lesions (pulmonary nodules) and evaluate the whole-lung/pulmonary malignancy. Currently, there are many studies about the first step, but few about the second step. Since the existence of nodule does not definitely indicate cancer, and the morphology of nodule has a complicated relationship with cancer, the diagnosis of lung cancer demands careful investigations on every suspicious nodule and integration of information of all nodules. We propose a 3D deep neural network to solve this problem. The model consists of two modules. The first one is a 3D region proposal network for nodule detection, which outputs all suspicious nodules for a subject. The second one selects the top five nodules based on the detection confidence, evaluates their cancer probabilities and combines them with a leaky noisy-or gate to obtain the probability of lung cancer for the subject. The two modules share the same backbone network, a modified U-net. The over-fitting caused by the shortage of training data is alleviated by training the two modules alternately. The proposed model won the first place in the Data Science Bowl 2017 competition. The code has been made publicly available.