# SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation

arXiv: https://arxiv.org/abs/2001.07645 · HTML: https://mlsanity.com/paper/2001.07645/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2020.

## Built on by (most cited first, 19 of 19)

- [Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods](https://mlsanity.com/paper/2111.02398/) (2021)
- [MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation](https://mlsanity.com/paper/2105.07451/) (2021)
- [Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models](https://mlsanity.com/paper/2310.03182/) (2023)
- [Explainable Medical Imaging AI Needs Human-Centered Design: Guidelines and Evidence from a Systematic Review](https://mlsanity.com/paper/2112.12596/) (2021)
- [Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks](https://mlsanity.com/paper/2410.02331/) (2024)
- [Explainable AI (XAI) in Image Segmentation in Medicine, Industry, and Beyond: A Survey](https://mlsanity.com/paper/2405.01636/) (2024)
- [Shape-intensity knowledge distillation for robust medical image segmentation](https://mlsanity.com/paper/2409.17503/) (2024)
- [TAX: Tendency-and-Assignment Explainer for Semantic Segmentation with Multi-Annotators](https://mlsanity.com/paper/2302.09561/) (2023)
- [First Full-Event Reconstruction from Imaging Atmospheric Cherenkov Telescope Real Data with Deep Learning](https://mlsanity.com/paper/2105.14927/) (2021)
- [Fully Non-Homogeneous Atmospheric Scattering Modeling with Convolutional Neural Networks for Single Image Dehazing](https://mlsanity.com/paper/2108.11292/) (2021)
- [MAProtoNet: A Multi-scale Attentive Interpretable Prototypical Part Network for 3D Magnetic Resonance Imaging Brain Tumor Classification](https://mlsanity.com/paper/2404.08917/) (2024)
- [RAD: Towards Trustworthy Retrieval-Augmented Multi-modal Clinical Diagnosis](https://mlsanity.com/paper/2509.19980/) (2025)
- [End-to-end lung nodule detection framework with model-based feature projection block](https://mlsanity.com/paper/2106.05741/) (2021)
- [PL-Net: Progressive Learning Network for Medical Image Segmentation](https://mlsanity.com/paper/2110.14484/) (2021)
- [Uncertainty-Based Ensemble Learning in CMR Semantic Segmentation](https://mlsanity.com/paper/2502.09269/) (2025)
- [Do Segmentation Models Understand Vascular Structure? A Blob-Based XAI Framework](https://mlsanity.com/paper/2504.11469/) (2025)
- [Beyond Forgetting in Continual Medical Image Segmentation: A Comprehensive Benchmark Study](https://mlsanity.com/paper/2605.06160/) (2026)
- [DepthPolyp: Pseudo-Depth Guided Lightweight Segmentation for Real-Time Colonoscopy](https://mlsanity.com/paper/2605.16519/) (2026)
- [Synthetic Aperture Radar Image Change Detection Based on Global Dynamic Context-Aware Network](https://mlsanity.com/paper/2605.16764/) (2026)

## Abstract (arXiv)

Medical image segmentation is a difficult but important task for many clinical operations such as cardiac bi-ventricular volume estimation. More recently, there has been a shift to utilizing deep learning and fully convolutional neural networks (CNNs) to perform image segmentation that has yielded state-of-the-art results in many public benchmark datasets. Despite the progress of deep learning in medical image segmentation, standard CNNs are still not fully adopted in clinical settings as they lack robustness and interpretability. Shapes are generally more meaningful features than solely textures of images, which are features regular CNNs learn, causing a lack of robustness. Likewise, previous works surrounding model interpretability have been focused on post hoc gradient-based saliency methods. However, gradient-based saliency methods typically require additional computations post hoc and have been shown to be unreliable for interpretability. Thus, we present a new architecture called Shape Attentive U-Net (SAUNet) which focuses on model interpretability and robustness. The proposed architecture attempts to address these limitations by the use of a secondary shape stream that captures rich shape-dependent information in parallel with the regular texture stream. Furthermore, we suggest multi-resolution saliency maps can be learned using our dual-attention decoder module which allows for multi-level interpretability and mitigates the need for additional computations post hoc. Our method also achieves state-of-the-art results on the two large public cardiac MRI image segmentation datasets of SUN09 and AC17.