# Advancing Lung Disease Diagnosis in 3D CT Scans

arXiv: https://arxiv.org/abs/2507.00993 · HTML: https://mlsanity.com/paper/2507.00993/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 30 references that are in the map.
- First posted on arXiv 1 Jul 2025.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Out-of-Distribution Generalization via Risk Extrapolation (REx)](https://mlsanity.com/paper/2003.00688/) (2020)
- [ResNeSt: Split-Attention Networks](https://mlsanity.com/paper/2004.08955/) (2020)
- [Deep Transparent Prediction through Latent Representation Analysis](https://mlsanity.com/paper/2009.07044/) (2020)
- [MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis](https://mlsanity.com/paper/2106.07524/) (2021)
- [A Deep Neural Architecture for Harmonizing 3-D Input Data Analysis and Decision Making in Medical Imaging](https://mlsanity.com/paper/2303.00175/) (2023)
- [Domain adaptation, Explainability & Fairness in AI for Medical Image Analysis: Diagnosis of COVID-19 based on 3-D Chest CT-scans](https://mlsanity.com/paper/2403.02192/) (2024)
- [COVID-19 Computer-aided Diagnosis through AI-assisted CT Imaging Analysis: Deploying a Medical AI System](https://mlsanity.com/paper/2403.06242/) (2024)
- [SAM2CLIP2SAM: Vision Language Model for Segmentation of 3D CT Scans for Covid-19 Detection](https://mlsanity.com/paper/2407.15728/) (2024)
- [CMC v2: Towards More Accurate COVID-19 Detection with Discriminative Video Priors](https://mlsanity.com/paper/2211.14557/) (2022)
- [Boosting COVID-19 Severity Detection with Infection-aware Contrastive Mixup Classification](https://mlsanity.com/paper/2211.14559/) (2022)
- [Advancing COVID-19 Detection in 3D CT Scans](https://mlsanity.com/paper/2403.11953/) (2024)
- [Strong Baseline and Bag of Tricks for COVID-19 Detection of CT Scans](https://mlsanity.com/paper/2303.08490/) (2023)

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

- [Clinical Priors Guided Lung Disease Detection in 3D CT Scans](https://mlsanity.com/paper/2603.15143/) (2026)
- [Vision-Language Model Based Multi-Expert Fusion for CT Image Classification](https://mlsanity.com/paper/2603.15154/) (2026)
- [Towards Fair and Robust Volumetric CT Classification via KL-Regularised Group Distributionally Robust Optimisation](https://mlsanity.com/paper/2603.15941/) (2026)
- [Robust Multi-Source Covid-19 Detection in CT Images](https://mlsanity.com/paper/2604.03320/) (2026)

## Abstract (arXiv)

To enable more accurate diagnosis of lung disease in chest CT scans, we propose a straightforward yet effective model. Firstly, we analyze the characteristics of 3D CT scans and remove non-lung regions, which helps the model focus on lesion-related areas and reduces computational cost. We adopt ResNeSt50 as a strong feature extractor, and use a weighted cross-entropy loss to mitigate class imbalance, especially for the underrepresented squamous cell carcinoma category. Our model achieves a Macro F1 Score of 0.80 on the validation set of the Fair Disease Diagnosis Challenge, demonstrating its strong performance in distinguishing between different lung conditions.