# Translational Lung Imaging Analysis Through Disentangled Representations

arXiv: https://arxiv.org/abs/2203.01668 · HTML: https://mlsanity.com/paper/2203.01668/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 38 references that are in the map.
- First posted on arXiv 3 Mar 2022.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019)
- [Object-Centric Learning with Slot Attention](https://mlsanity.com/paper/2006.15055/) (2020)
- [Improving Variational Inference with Inverse Autoregressive Flow](https://mlsanity.com/paper/1606.04934/) (2016)
- [NVAE: A Deep Hierarchical Variational Autoencoder](https://mlsanity.com/paper/2007.03898/) (2020)
- [Inductive Biases for Deep Learning of Higher-Level Cognition](https://mlsanity.com/paper/2011.15091/) (2020)
- [Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images](https://mlsanity.com/paper/2011.10650/) (2020)
- [A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises](https://mlsanity.com/paper/2008.09104/) (2020)
- [Deep Structural Causal Models for Tractable Counterfactual Inference](https://mlsanity.com/paper/2006.06485/) (2020)
- [Causality matters in medical imaging](https://mlsanity.com/paper/1912.08142/) (2019)
- [A guide to convolution arithmetic for deep learning](https://mlsanity.com/paper/1603.07285/) (2016)
- [Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans](https://mlsanity.com/paper/2008.06388/) (2020)
- [How to represent part-whole hierarchies in a neural network](https://mlsanity.com/paper/2102.12627/) (2021)
- [COVID-19 Image Data Collection](https://mlsanity.com/paper/2003.11597/) (2020)
- [Using StyleGAN for Visual Interpretability of Deep Learning Models on Medical Images](https://mlsanity.com/paper/2101.07563/) (2021)
- [A Multi-Task Self-Normalizing 3D-CNN to Infer Tuberculosis Radiological Manifestations](https://mlsanity.com/paper/1907.12331/) (2019)

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

- [Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectives](https://mlsanity.com/paper/2307.16851/) (2023)
- [A Review of Causality for Learning Algorithms in Medical Image Analysis](https://mlsanity.com/paper/2206.05498/) (2022)
- [Learning Generic Lung Ultrasound Biomarkers for Decoupling Feature Extraction from Downstream Tasks](https://mlsanity.com/paper/2206.08398/) (2022)
- [Causal Transfer in Medical Image Analysis](https://mlsanity.com/paper/2603.24388/) (2026)

## Abstract (arXiv)

The development of new treatments often requires clinical trials with translational animal models using (pre)-clinical imaging to characterize inter-species pathological processes. Deep Learning (DL) models are commonly used to automate retrieving relevant information from the images. Nevertheless, they typically suffer from low generability and explainability as a product of their entangled design, resulting in a specific DL model per animal model. Consequently, it is not possible to take advantage of the high capacity of DL to discover statistical relationships from inter-species images. To alleviate this problem, in this work, we present a model capable of extracting disentangled information from images of different animal models and the mechanisms that generate the images. Our method is located at the intersection between deep generative models, disentanglement and causal representation learning. It is optimized from images of pathological lung infected by Tuberculosis and is able: a) from an input slice, infer its position in a volume, the animal model to which it belongs, the damage present and even more, generate a mask covering the whole lung (similar overlap measures to the nnU-Net), b) generate realistic lung images by setting the above variables and c) generate counterfactual images, namely, healthy versions of a damaged input slice.