Abstract
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: this https URL.
Abstract (translated)
弱监督异常检测（WSAD）在脑MRI扫描中的应用是一项重要的挑战，特别是在精确的像素级异常标注不可用且仅能获取到弱标签（如切片级别）的情况下。为了快速准确地识别大脑异常情况，在这项工作中，我们提出了一种新的两阶段WSAD框架——RASALoRE（基于位置随机嵌入的位置感知空间注意力机制）。该框架分为两个阶段： 在第一阶段，我们引入了判别性双提示调优（Discriminative Dual Prompt Tuning, DDPT）机制，能够根据切片级别的标签生成高质量的伪弱掩模，作为粗略定位线索。 第二阶段中，我们提出了一种基于位置感知空间注意力机制的分割网络，并使用固定的位置随机嵌入。这种设计使得模型能够在异常区域有效聚焦。 我们的方法在多种数据集上实现了最先进的异常检测性能，在参数量小于800万的情况下显著优于现有的WSAD方法。对BraTS20、BraTS21、BraTS23和MSD等数据集的广泛评估显示，该模型不仅提高了性能还大幅减少了计算复杂度。 代码可在以下链接获取：[提供URL]
URL
https://arxiv.org/abs/2510.08052
