# Computer-aided diagnosis of lung carcinoma using deep learning - a pilot study

arXiv: https://arxiv.org/abs/1803.05471 · HTML: https://mlsanity.com/paper/1803.05471/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2018.

## Built on by (most cited first, 4 of 4)

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- [Evaluation of Multi-Scale Multiple Instance Learning to Improve Thyroid Cancer Classification](https://mlsanity.com/paper/2204.10942/) (2022)
- [[Citation needed] Data usage and citation practices in medical imaging conferences](https://mlsanity.com/paper/2402.03003/) (2024)
- [Histopathology WSI Encoding based on GCNs for Scalable and Efficient Retrieval of Diagnostically Relevant Regions](https://mlsanity.com/paper/2104.07878/) (2021)

## Abstract (arXiv)

Aim: Early detection and correct diagnosis of lung cancer are the most important steps in improving patient outcome. This study aims to assess which deep learning models perform best in lung cancer diagnosis. Methods: Non-small cell lung carcinoma and small cell lung carcinoma biopsy specimens were consecutively obtained and stained. The specimen slides were diagnosed by two experienced pathologists (over 20 years). Several deep learning models were trained to discriminate cancer and non-cancer biopsies. Result: Deep learning models give reasonable AUC from 0.8810 to 0.9119. Conclusion: The deep learning analysis could help to speed up the detection process for the whole-slide image (WSI) and keep the comparable detection rate with human observer.