# Machine Learning Applications in Lung Cancer Diagnosis, Treatment and Prognosis

arXiv: https://arxiv.org/abs/2203.02794 · HTML: https://mlsanity.com/paper/2203.02794/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 226 references that are in the map.
- First posted on arXiv 5 Mar 2022.

## Builds on (most cited first, 27 of 27)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [Learning Deconvolution Network for Semantic Segmentation](https://mlsanity.com/paper/1505.04366/) (2015)
- [Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation](https://mlsanity.com/paper/1603.05959/) (2016)
- [OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks](https://mlsanity.com/paper/1312.6229/) (2013)
- [Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes](https://mlsanity.com/paper/1611.08323/) (2016)
- [Micro-Net: A unified model for segmentation of various objects in microscopy images](https://mlsanity.com/paper/1804.08145/) (2018)
- [ImageGCN: Multi-Relational Image Graph Convolutional Networks for Disease Identification with Chest X-rays](https://mlsanity.com/paper/1904.00325/) (2019)
- [Risk Stratification of Lung Nodules Using 3D CNN-Based Multi-task Learning](https://mlsanity.com/paper/1704.08797/) (2017)
- [A Collaborative Computer Aided Diagnosis (C-CAD) System with Eye-Tracking, Sparse Attentional Model, and Deep Learning](https://mlsanity.com/paper/1802.06260/) (2018)
- [Diet Networks: Thin Parameters for Fat Genomics](https://mlsanity.com/paper/1611.09340/) (2016)
- [Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation](https://mlsanity.com/paper/1608.03235/) (2016)
- [Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images](https://mlsanity.com/paper/1704.00406/) (2017)
- [Deep Semi Supervised Generative Learning for Automated PD-L1 Tumor Cell Scoring on NSCLC Tissue Needle Biopsies](https://mlsanity.com/paper/1806.11036/) (2018)
- [Bayesian Consensus Clustering](https://mlsanity.com/paper/1302.7280/) (2013)
- [Deep Generative Classifiers for Thoracic Disease Diagnosis with Chest X-ray Images](https://mlsanity.com/paper/1809.07436/) (2018)
- [PANTHER: Pathway Augmented Nonnegative Tensor factorization for HighER-order feature learning](https://mlsanity.com/paper/2012.08580/) (2020)
- [Characterization of Lung Nodule Malignancy using Hybrid Shape and Appearance Features](https://mlsanity.com/paper/1609.06668/) (2016)
- [Cancer classification and pathway discovery using non-negative matrix factorization](https://mlsanity.com/paper/1809.10681/) (2018)
- [Supervised Nonnegative Matrix Factorization to Predict ICU Mortality Risk](https://mlsanity.com/paper/1809.10680/) (2018)

## Abstract (arXiv)

The recent development of imaging and sequencing technologies enables systematic advances in the clinical study of lung cancer. Meanwhile, the human mind is limited in effectively handling and fully utilizing the accumulation of such enormous amounts of data. Machine learning-based approaches play a critical role in integrating and analyzing these large and complex datasets, which have extensively characterized lung cancer through the use of different perspectives from these accrued data. In this article, we provide an overview of machine learning-based approaches that strengthen the varying aspects of lung cancer diagnosis and therapy, including early detection, auxiliary diagnosis, prognosis prediction and immunotherapy practice. Moreover, we highlight the challenges and opportunities for future applications of machine learning in lung cancer.