# Mutual Information-based Disentangled Neural Networks for Classifying Unseen Categories in Different Domains: Application to Fetal Ultrasound Imaging

arXiv: https://arxiv.org/abs/2011.00739 · HTML: https://mlsanity.com/paper/2011.00739/

## Key facts

- Cited by 15 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 30 Oct 2020.

## Built on by (most cited first, 15 of 15)

- [Domain Generalization for Medical Image Analysis: A Review](https://mlsanity.com/paper/2310.08598/) (2023)
- [Learning Disentangled Representations in the Imaging Domain](https://mlsanity.com/paper/2108.12043/) (2021)
- [A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysis](https://mlsanity.com/paper/2201.12260/) (2022)
- [Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives](https://mlsanity.com/paper/2308.01265/) (2023)
- [MI-SegNet: Mutual Information-Based US Segmentation for Unseen Domain Generalization](https://mlsanity.com/paper/2303.12649/) (2023)
- [AtrialGeneral: Domain Generalization for Left Atrial Segmentation of Multi-Center LGE MRIs](https://mlsanity.com/paper/2106.08727/) (2021)
- [A New Semi-supervised Learning Benchmark for Classifying View and Diagnosing Aortic Stenosis from Echocardiograms](https://mlsanity.com/paper/2108.00080/) (2021)
- [Intelligent Robotic Sonographer: Mutual Information-based Disentangled Reward Learning from Few Demonstrations](https://mlsanity.com/paper/2307.03705/) (2023)
- [Dual-Stream Reciprocal Disentanglement Learning for Domain Adaptation Person Re-Identification](https://mlsanity.com/paper/2106.13929/) (2021)
- [DopUS-Net: Quality-Aware Robotic Ultrasound Imaging based on Doppler Signal](https://mlsanity.com/paper/2305.08938/) (2023)
- [Decomposing Normal and Abnormal Features of Medical Images into Discrete Latent Codes for Content-Based Image Retrieval](https://mlsanity.com/paper/2103.12328/) (2021)
- [Adnexal Mass Segmentation with Ultrasound Data Synthesis](https://mlsanity.com/paper/2209.12305/) (2022)
- [A Dual Adversarial Calibration Framework for Automatic Fetal Brain Biometry](https://mlsanity.com/paper/2108.12719/) (2021)
- [Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation](https://mlsanity.com/paper/2406.02336/) (2024)
- [Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation](https://mlsanity.com/paper/2507.23326/) (2025)

## Abstract (arXiv)

Deep neural networks exhibit limited generalizability across images with different entangled domain features and categorical features. Learning generalizable features that can form universal categorical decision boundaries across domains is an interesting and difficult challenge. This problem occurs frequently in medical imaging applications when attempts are made to deploy and improve deep learning models across different image acquisition devices, across acquisition parameters or if some classes are unavailable in new training databases. To address this problem, we propose Mutual Information-based Disentangled Neural Networks (MIDNet), which extract generalizable categorical features to transfer knowledge to unseen categories in a target domain. The proposed MIDNet adopts a semi-supervised learning paradigm to alleviate the dependency on labeled data. This is important for real-world applications where data annotation is time-consuming, costly and requires training and expertise. We extensively evaluate the proposed method on fetal ultrasound datasets for two different image classification tasks where domain features are respectively defined by shadow artifacts and image acquisition devices. Experimental results show that the proposed method outperforms the state-of-the-art on the classification of unseen categories in a target domain with sparsely labeled training data.