# Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks

arXiv: https://arxiv.org/abs/1901.11489 · HTML: https://mlsanity.com/paper/1901.11489/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2019.

## Built on by (most cited first, 30 of 43)

- [A graph-transformer for whole slide image classification](https://mlsanity.com/paper/2205.09671/) (2022)
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- [Masked Pre-Training of Transformers for Histology Image Analysis](https://mlsanity.com/paper/2304.07434/) (2023)
- [Resolution-Based Distillation for Efficient Histology Image Classification](https://mlsanity.com/paper/2101.04170/) (2021)
- [Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy](https://mlsanity.com/paper/2306.07999/) (2023)
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- [CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns](https://mlsanity.com/paper/2501.08094/) (2025)
- [HistoPerm: A Permutation-Based View Generation Approach for Improving Histopathologic Feature Representation Learning](https://mlsanity.com/paper/2209.06185/) (2022)
- [M2Fusion: Bayesian-based Multimodal Multi-level Fusion on Colorectal Cancer Microsatellite Instability Prediction](https://mlsanity.com/paper/2401.07854/) (2024)
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- [Towards Highly Expressive Machine Learning Models of Non-Melanoma Skin Cancer](https://mlsanity.com/paper/2207.05749/) (2022)
- [Predictive uncertainty estimation in deep learning for lung carcinoma classification in digital pathology under real dataset shifts](https://mlsanity.com/paper/2408.08432/) (2024)
- [A Novel Framework for the Automated Characterization of Gram-Stained Blood Culture Slides Using a Large-Scale Vision Transformer](https://mlsanity.com/paper/2409.15546/) (2024)
- [Predicting Targeted Therapy Resistance in Non-Small Cell Lung Cancer Using Multimodal Machine Learning](https://mlsanity.com/paper/2503.24165/) (2025)
- [A deep learning model for gastric diffuse-type adenocarcinoma classification in whole slide images](https://mlsanity.com/paper/2104.12478/) (2021)
- [Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network](https://mlsanity.com/paper/2110.14728/) (2021)
- [AI-based Carcinoma Detection and Classification Using Histopathological Images: A Systematic Review](https://mlsanity.com/paper/2201.07231/) (2022)
- [Preparing data for pathological artificial intelligence with clinical-grade performance](https://mlsanity.com/paper/2205.10748/) (2022)

## Abstract (arXiv)

Classification of histologic patterns in lung adenocarcinoma is critical for determining tumor grade and treatment for patients. However, this task is often challenging due to the heterogeneous nature of lung adenocarcinoma and the subjective criteria for evaluation. In this study, we propose a deep learning model that automatically classifies the histologic patterns of lung adenocarcinoma on surgical resection slides. Our model uses a convolutional neural network to identify regions of neoplastic cells, then aggregates those classifications to infer predominant and minor histologic patterns for any given whole-slide image. We evaluated our model on an independent set of 143 whole-slide images. It achieved a kappa score of 0.525 and an agreement of 66.6% with three pathologists for classifying the predominant patterns, slightly higher than the inter-pathologist kappa score of 0.485 and agreement of 62.7% on this test set. All evaluation metrics for our model and the three pathologists were within 95% confidence intervals of agreement. If confirmed in clinical practice, our model can assist pathologists in improving classification of lung adenocarcinoma patterns by automatically pre-screening and highlighting cancerous regions prior to review. Our approach can be generalized to any whole-slide image classification task, and code is made publicly available at https://github.com/BMIRDS/deepslide.