统一框架提升超声图像分析鲁棒性，跨设备和任务表现更稳定。
AURORA: Adaptive Unified Representation for Robust Ultrasound Analysis
- 基于Qwen3-VL视觉编码器构建统一表示，融合多尺度特征实现像素级与全局推理
- 在多个任务上验证准确率从67%提升至85%，测试集平均得分达81.84%
- 结构轻量适配性强，适合医疗影像中复杂场景下的多任务分析
超声图像因设备、操作者和解剖部位差异大，导致模型泛化能力差。为应对这一挑战，我们提出AURORA——一种基于Qwen3-VL系列视觉编码器的统一多任务框架。通过将中间令牌特征映射为空间特征图，并使用轻量级多尺度特征金字塔进行融合，实现共享表示下的像素级预测与全局推理。各任务由小型专用预测头处理，训练采用任务感知采样与选择性损失平衡策略，缓解监督异构性和任务不平衡问题。方法设计简洁，易于优化，适用于广泛超声分析任务。验证集准确率从67%提升至85%，官方测试集所有任务平均得分为81.84%。代码已公开：https://github.com/saitejalekkala33/FMCUIA-ISBI.git
原文摘要 · Abstract (English)
Ultrasound images vary widely across scanners, operators, and anatomical targets, which often causes models trained in one setting to generalize poorly to new hospitals and clinical conditions. The Foundation Model Challenge for Ultrasound Image Analysis (FMC-UIA) reflects this difficulty by requiring a single model to handle multiple tasks, including segmentation, detection, classification, and landmark regression across diverse organs and datasets. We propose a unified multi-task framework based on a transformer visual encoder from the Qwen3-VL family. Intermediate token features are projected into spatial feature maps and fused using a lightweight multi-scale feature pyramid, enabling both pixel-level predictions and global reasoning within a shared representation. Each task is handled by a small task-specific prediction head, while training uses task-aware sampling and selective loss balancing to manage heterogeneous supervision and reduce task imbalance. Our method is designed to be simple to optimize and adaptable across a wide range of ultrasound analysis tasks. The performance improved from 67% to 85% on the validation set and achieved an average score of 81.84% on the official test set across all tasks. The code is publicly available at: https://github.com/saitejalekkala33/FMCUIA-ISBI.git
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