Lung Nodule Classification

8 papers with code • 1 benchmarks • 1 datasets

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Datasets


Latest papers with no code

Are Deep Learning Classification Results Obtained on CT Scans Fair and Interpretable?

no code yet • 22 Sep 2023

For example, most lung nodule classification papers using deep learning randomly shuffle data and split it into training, validation, and test sets, causing certain images from the CT scan of a person to be in the training set, while other images of the exact same person to be in the validation or testing image sets.

Wavelet leader based formalism to compute multifractal features for classifying lung nodules in X-ray images

no code yet • 1 Jul 2022

This paper presents and validates a novel lung nodule classification algorithm that uses multifractal features found in X-ray images.

Deep fusion of gray level co-occurrence matrices for lung nodule classification

no code yet • 10 May 2022

The proposed methods are trained and assessed through the LIDC-IDRI dataset, where 94. 4%, 91. 6%, and 95. 8% Accuracy, sensitivity, and specificity are obtained, respectively for 2D-GLCM fusion and 97. 33%, 96%, and 98%, accuracy, sensitivity, and specificity, respectively, for 2. 5D-GLCM fusion.

Faithful learning with sure data for lung nodule diagnosis

no code yet • 25 Feb 2022

In this study, we construct a sure dataset with pathologically-confirmed labels and propose a collaborative learning framework to facilitate sure nodule classification by integrating unsure data knowledge through nodule segmentation and malignancy score regression.

The Pitfalls of Sample Selection: A Case Study on Lung Nodule Classification

no code yet • 11 Aug 2021

Using publicly available data to determine the performance of methodological contributions is important as it facilitates reproducibility and allows scrutiny of the published results.

The Effect of the Loss on Generalization: Empirical Study on Synthetic Lung Nodule Data

no code yet • 10 Aug 2021

Convolutional Neural Networks (CNNs) are widely used for image classification in a variety of fields, including medical imaging.

Lung Cancer Diagnosis Using Deep Attention Based on Multiple Instance Learning and Radiomics

no code yet • 29 Apr 2021

In this article, we treat lung cancer diagnosis as a multiple instance learning (MIL) problem in order to better reflect the diagnosis process in the clinical setting and for the higher interpretability of the output.

Meta ordinal weighting net for improving lung nodule classification

no code yet • 31 Jan 2021

In this paper, we propose a Meta Ordinal Weighting Network (MOW-Net) to explicitly align each training sample with a meta ordinal set (MOS) containing a few samples from all classes.

3D Axial-Attention for Lung Nodule Classification

no code yet • 28 Dec 2020

Methods: We propose to use 3D Axial-Attention, which requires a fraction of the computing power of a regular Non-Local network (i. e., self-attention).

Meta Ordinal Regression Forest For Learning with Unsure Lung Nodules

no code yet • 7 Dec 2020

Recently, an unsure data model (UDM) was proposed to incorporate those unsure nodules by formulating this problem as an ordinal regression, showing better performance over traditional binary classification.