# Technical Report: Quality Assessment Tool for Machine Learning with Clinical CT

arXiv: https://arxiv.org/abs/2107.12842 · HTML: https://mlsanity.com/paper/2107.12842/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 39 references that are in the map.
- First posted on arXiv 27 Jul 2021.

## Builds on (most cited first, 7 of 7)

- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network](https://mlsanity.com/paper/1711.08324/) (2017)
- [Iterative fully convolutional neural networks for automatic vertebra segmentation and identification](https://mlsanity.com/paper/1804.04383/) (2018)
- [Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning](https://mlsanity.com/paper/1810.12185/) (2018)
- [Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions](https://mlsanity.com/paper/1711.00349/) (2017)
- [Deep Multi-path Network Integrating Incomplete Biomarker and Chest CT Data for Evaluating Lung Cancer Risk](https://mlsanity.com/paper/2010.09524/) (2020)
- [Deep Multi-task Prediction of Lung Cancer and Cancer-free Progression from Censored Heterogenous Clinical Imaging](https://mlsanity.com/paper/1911.05115/) (2019)

## Built on by (most cited first, 1 of 1)

- [Cohort-Aware Agents for Individualized Lung Cancer Risk Prediction Using a Retrieval-Augmented Model Selection Framework](https://mlsanity.com/paper/2508.14940/) (2025)

## Abstract (arXiv)

Image Quality Assessment (IQA) is important for scientific inquiry, especially in medical imaging and machine learning. Potential data quality issues can be exacerbated when human-based workflows use limited views of the data that may obscure digital artifacts. In practice, multiple factors such as network issues, accelerated acquisitions, motion artifacts, and imaging protocol design can impede the interpretation of image collections. The medical image processing community has developed a wide variety of tools for the inspection and validation of imaging data. Yet, IQA of computed tomography (CT) remains an under-recognized challenge, and no user-friendly tool is commonly available to address these potential issues. Here, we create and illustrate a pipeline specifically designed to identify and resolve issues encountered with large-scale data mining of clinically acquired CT data. Using the widely studied National Lung Screening Trial (NLST), we have identified approximately 4% of image volumes with quality concerns out of 17,392 scans. To assess robustness, we applied the proposed pipeline to our internal datasets where we find our tool is generalizable to clinically acquired medical images. In conclusion, the tool has been useful and time-saving for research study of clinical data, and the code and tutorials are publicly available at https://github.com/MASILab/QA_tool.