# Interpretable Similarity of Synthetic Image Utility

arXiv: https://arxiv.org/abs/2512.17080 · HTML: https://mlsanity.com/paper/2512.17080/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 47 references that are in the map.
- First posted on arXiv 18 Dec 2025.

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification](https://mlsanity.com/paper/2110.14795/) (2021)
- [Multiple Instance Learning: A Survey of Problem Characteristics and Applications](https://mlsanity.com/paper/1612.03365/) (2016)
- [Pros and Cons of GAN Evaluation Measures: New Developments](https://mlsanity.com/paper/2103.09396/) (2021)
- [Video Polyp Segmentation: A Deep Learning Perspective](https://mlsanity.com/paper/2203.14291/) (2022)
- [A multi-centre polyp detection and segmentation dataset for generalisability assessment](https://mlsanity.com/paper/2106.04463/) (2021)
- [Are Natural Domain Foundation Models Useful for Medical Image Classification?](https://mlsanity.com/paper/2310.19522/) (2023)
- [Feature Extraction for Generative Medical Imaging Evaluation: New Evidence Against an Evolving Trend](https://mlsanity.com/paper/2311.13717/) (2023)
- [Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review of Recent Developments and Challenges](https://mlsanity.com/paper/2407.00116/) (2024)
- [Stable Deep MRI Reconstruction using Generative Priors](https://mlsanity.com/paper/2210.13834/) (2022)
- [Interactive Medical Image Analysis with Concept-based Similarity Reasoning](https://mlsanity.com/paper/2503.06873/) (2025)
- [This Intestine Does Not Exist: Multiscale Residual Variational Autoencoder for Realistic Wireless Capsule Endoscopy Image Generation](https://mlsanity.com/paper/2302.02150/) (2023)
- [Evaluating clinical diversity and plausibility of synthetic capsule endoscopic images](https://mlsanity.com/paper/2301.06366/) (2023)
- [CosSIF: Cosine similarity-based image filtering to overcome low inter-class variation in synthetic medical image datasets](https://mlsanity.com/paper/2307.13842/) (2023)
- [Clinical Evaluation of Medical Image Synthesis: A Case Study in Wireless Capsule Endoscopy](https://mlsanity.com/paper/2411.00178/) (2024)
- [Building a Synthetic Vascular Model: Evaluation in an Intracranial Aneurysms Detection Scenario](https://mlsanity.com/paper/2411.02477/) (2024)
- [E Pluribus Unum Interpretable Convolutional Neural Networks](https://mlsanity.com/paper/2208.05369/) (2022)
- [Uncertainty-Aware Regularization for Image-to-Image Translation](https://mlsanity.com/paper/2412.01705/) (2024)

## Built on by (most cited first, 2 of 2)

- [EndoFSA: Endoscopic Few-Shot Image Generation via Rank-Constrained Parameter Adaptation](https://mlsanity.com/paper/2609.29930/) (2026)
- [From Image Latent Space to Fuzzy Rules: Interpretable Analysis of Gastrointestinal Foundation Model](https://mlsanity.com/paper/2610.00414/) (2026)

## Abstract (arXiv)

Synthetic medical image data can unlock the potential of deep learning (DL)-based clinical decision support (CDS) systems through the creation of large scale, privacy-preserving, training sets. Despite the significant progress in this field, there is still a largely unanswered research question: "How can we quantitatively assess the similarity of a synthetically generated set of images with a set of real images in a given application domain?". Today, answers to this question are mainly provided via user evaluation studies, inception-based measures, and the classification performance achieved on synthetic images. This paper proposes a novel measure to assess the similarity between synthetically generated and real sets of images, in terms of their utility for the development of DL-based CDS systems. Inspired by generalized neural additive models, and unlike inception-based measures, the proposed measure is interpretable (Interpretable Utility Similarity, IUS), explaining why a synthetic dataset could be more useful than another one in the context of a CDS system based on clinically relevant image features. The experimental results on publicly available benchmark datasets from various color medical imaging modalities including endoscopic, dermoscopic and fundus imaging, indicate that selecting synthetic images of high utility similarity using IUS can result in relative improvements of up to 54.6% in terms of classification performance. The generality of IUS for synthetic data assessment is demonstrated also for grayscale X-ray and ultrasound imaging modalities. IUS implementation is available at https://github.com/innoisys/ius.