# Segmentation of Non-Small Cell Lung Carcinomas: Introducing DRU-Net and Multi-Lens Distortion

arXiv: https://arxiv.org/abs/2406.14287 · HTML: https://mlsanity.com/paper/2406.14287/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 86 references that are in the map.
- First posted on arXiv 20 Jun 2024.

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

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- [Deep Multi-Magnification Networks for Multi-Class Breast Cancer Image Segmentation](https://mlsanity.com/paper/1910.13042/) (2019)
- [FHIST: A Benchmark for Few-shot Classification of Histological Images](https://mlsanity.com/paper/2206.00092/) (2022)
- [ScanNet: A Fast and Dense Scanning Framework for Metastatic Breast Cancer Detection from Whole-Slide Images](https://mlsanity.com/paper/1707.09597/) (2017)
- [FastPathology: An open-source platform for deep learning-based research and decision support in digital pathology](https://mlsanity.com/paper/2011.06033/) (2020)
- [A Task-aware Dual Similarity Network for Fine-grained Few-shot Learning](https://mlsanity.com/paper/2210.12348/) (2022)

## Abstract (arXiv)

Considering the increased workload in pathology laboratories today, automated tools such as artificial intelligence models can help pathologists with their tasks and ease the workload. In this paper, we are proposing a segmentation model (DRU-Net) that can provide a delineation of human non-small cell lung carcinomas and an augmentation method that can improve classification results. The proposed model is a fused combination of truncated pre-trained DenseNet201 and ResNet101V2 as a patch-wise classifier followed by a lightweight U-Net as a refinement model. We have used two datasets (Norwegian Lung Cancer Biobank and Haukeland University Hospital lung cancer cohort) to create our proposed model. The DRU-Net model achieves an average of 0.91 Dice similarity coefficient. The proposed spatial augmentation method (multi-lens distortion) improved the network performance by 3%. Our findings show that choosing image patches that specifically include regions of interest leads to better results for the patch-wise classifier compared to other sampling methods. The qualitative analysis showed that the DRU-Net model is generally successful in detecting the tumor. On the test set, some of the cases showed areas of false positive and false negative segmentation in the periphery, particularly in tumors with inflammatory and reactive changes.