# Prototype Memory-Guided Training-Free Anomaly Classification and Localization in Prenatal Ultrasound

arXiv: https://arxiv.org/abs/2607.00744 · HTML: https://mlsanity.com/paper/2607.00744/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 28 references that are in the map.
- First posted on arXiv 1 Jul 2026.

## Builds on (most cited first, 18 of 18)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [DINOv3](https://mlsanity.com/paper/2508.10104/) (2025)
- [DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection](https://mlsanity.com/paper/2108.09017/) (2021)
- [DiGeo: Discriminative Geometry-Aware Learning for Generalized Few-Shot Object Detection](https://mlsanity.com/paper/2303.09674/) (2023)
- [JAFAR: Jack up Any Feature at Any Resolution](https://mlsanity.com/paper/2506.11136/) (2025)
- [Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound](https://mlsanity.com/paper/2007.15273/) (2020)
- [Breaking Immutable: Information-Coupled Prototype Elaboration for Few-Shot Object Detection](https://mlsanity.com/paper/2211.14782/) (2022)
- [ProtoSAM: One-Shot Medical Image Segmentation With Foundational Models](https://mlsanity.com/paper/2407.07042/) (2024)
- [SynPo: Boosting Training-Free Few-Shot Medical Segmentation via High-Quality Negative Prompts](https://mlsanity.com/paper/2506.15153/) (2025)
- [SMILe: Leveraging Submodular Mutual Information For Robust Few-Shot Object Detection](https://mlsanity.com/paper/2407.02665/) (2024)
- [MAUP: Training-free Multi-center Adaptive Uncertainty-aware Prompting for Cross-domain Few-shot Medical Image Segmentation](https://mlsanity.com/paper/2508.03511/) (2025)
- [Does DINOv3 Set a New Medical Vision Standard? Benchmarking 2D and 3D Classification, Segmentation, and Registration](https://mlsanity.com/paper/2509.06467/) (2025)
- [Medical-Knowledge Driven Multiple Instance Learning for Classifying Severe Abdominal Anomalies on Prenatal Ultrasound](https://mlsanity.com/paper/2507.01401/) (2025)
- [MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation](https://mlsanity.com/paper/2508.11032/) (2025)
- [Artificial Intelligence for Detecting Fetal Orofacial Clefts and Advancing Medical Education](https://mlsanity.com/paper/2603.06522/) (2026)
- [Uncertainty-aware Diffusion and Reinforcement Learning for Joint Plane Localization and Anomaly Diagnosis in 3D Ultrasound](https://mlsanity.com/paper/2506.23538/) (2025)

## Built on by (most cited first, 1 of 1)

- [Cross-Modal Ultrasound-MRI Learning for Fetal Brain Ventricular Volumetry and Abnormality Screening](https://mlsanity.com/paper/2608.14763/) (2026)

## Abstract (arXiv)

Prenatal anomaly classification and localization is of critical importance for fetal health and pregnancy management. Although ultrasound (US) is the primary modality for prenatal screening, accurate diagnosis remains challenging due to the low prevalence and high heterogeneity of anomalies. Existing deep learning methods for prenatal tasks rely on large-scale annotated datasets, which are difficult to obtain in practice. Although few-shot learning alleviates data scarcity, it typically requires fine-tuning for new categories, limiting its practicality in resource-limited clinical settings. To address these challenges, we propose a training-free framework for multi-class prenatal US anomaly classification and localization that operates with only a few reference images per class, representing the first exploration of this setting. Our framework comprises three key components: (1) a memory bank with multi-granular prototypes that explicitly models both class-level semantics and anomaly characteristics; (2) a prototype-driven soft merging mechanism that aggregates discriminative features to detect the anomaly region; and (3) a class-aware refinement strategy that leverages prototype consistency to improve category prediction. Extensively validated on a multi-center prenatal US dataset containing 1,149 cases, with a total of 2,357 images and 9 categories, our proposed method outperforms the competitors.