用位置嵌入增强注意力，实现脑MRI弱监督异常检测
RASALoRE: Region Aware Spatial Attention with Location-based Random Embeddings for Weakly Supervised Anomaly Detection in Brain MRI Scans
- 基于切片级标签生成伪掩码，提供粗粒度定位线索
- 采用区域感知注意力与固定位置随机嵌入，精准聚焦异常区域
- 参数少于800万，性能超越现有方法，适合临床快速筛查
脑部MRI中的弱监督异常检测（WSAD）在缺乏像素级标注、仅拥有切片级标签时仍能实现快速准确的异常识别，具有重要应用价值。本文提出RASALoRE：一种两阶段新型框架，第一阶段引入判别性双提示调优（DDPT）机制，基于切片级标签生成高质量伪弱掩码，作为粗略定位依据；第二阶段设计一个带有区域感知空间注意力机制的分割网络，依赖固定的位置基随机嵌入，使模型能有效聚焦异常区域。该方法在BraTS20、BraTS21、BraTS23和MSD数据集上均取得当前最优性能，同时参数量低于800万，计算复杂度显著降低。代码已开源。
原文摘要 · Abstract (English)
Weakly Supervised Anomaly detection (WSAD) in brain MRI scans is an important challenge useful to obtain quick and accurate detection of brain anomalies when precise pixel-level anomaly annotations are unavailable and only weak labels (e.g., slice-level) are available. In this work, we propose RASALoRE: Region Aware Spatial Attention with Location-based Random Embeddings, a novel two-stage WSAD framework. In the first stage, we introduce a Discriminative Dual Prompt Tuning (DDPT) mechanism that generates high-quality pseudo weak masks based on slice-level labels, serving as coarse localization cues. In the second stage, we propose a segmentation network with a region-aware spatial attention mechanism that relies on fixed location-based random embeddings. This design enables the model to effectively focus on anomalous regions. Our approach achieves state-of-the-art anomaly detection performance, significantly outperforming existing WSAD methods while utilizing less than 8 million parameters. Extensive evaluations on the BraTS20, BraTS21, BraTS23, and MSD datasets demonstrate a substantial performance improvement coupled with a significant reduction in computational complexity. Code is available at: https://github.com/BheeshmSharma/RASALoRE-BMVC-2025/.
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