用可学习查询提升ViT适配器，精准定位超声图像中的胆囊癌病灶。
LQ-Adapter: ViT-Adapter with Learnable Queries for Gallbladder Cancer Detection from Ultrasound Image
- 设计可学习内容查询替代传统空间先验，增强小病灶定位能力
- 在胆囊癌检测数据集上mIoU提升5.4%以上，达新SOTA
- 跨数据集验证有效，适用于不同医学影像分割任务
本文研究超声图像中胆囊癌（GBC）的检测问题。由于噪声、纹理和视角变化导致图像质量低，现代深度神经网络面临挑战，需精确定位以识别恶性特征。现有方法多采用复杂定制架构。受基础模型与适配器思想启发，我们探究ViT-Adapter在该任务中的表现。发现其依赖的基于CNN的空间先验模块在小病灶、不规则结构场景下效率低下。为此提出LQ-Adapter，通过在基础空间先验模块上引入可学习内容查询，提升定位性能。在超声图像基GBC检测数据集上，相较ViT-Adapter、DINO和FocalNet-DINO，mIoU分别提升5.4%、5.8%和2.7%，达到新SOTA。同时在Kvasir-Seg结肠镜图像息肉检测数据集上验证有效性，证明其在多种医学影像任务中的泛化能力。代码已开源。
原文摘要 · Abstract (English)
We focus on the problem of Gallbladder Cancer (GBC) detection from Ultrasound (US) images. The problem presents unique challenges to modern Deep Neural Network (DNN) techniques due to low image quality arising from noise, textures, and viewpoint variations. Tackling such challenges would necessitate precise localization performance by the DNN to identify the discerning features for the downstream malignancy prediction. While several techniques have been proposed in the recent years for the problem, all of these methods employ complex custom architectures. Inspired by the success of foundational models for natural image tasks, along with the use of adapters to fine-tune such models for the custom tasks, we investigate the merit of one such design, ViT-Adapter, for the GBC detection problem. We observe that ViT-Adapter relies predominantly on a primitive CNN-based spatial prior module to inject the localization information via cross-attention, which is inefficient for our problem due to the small pathology sizes, and variability in their appearances due to non-regular structure of the malignancy. In response, we propose, LQ-Adapter, a modified Adapter design for ViT, which improves localization information by leveraging learnable content queries over the basic spatial prior module. Our method surpasses existing approaches, enhancing the mean IoU (mIoU) scores by 5.4%, 5.8%, and 2.7% over ViT-Adapters, DINO, and FocalNet-DINO, respectively on the US image-based GBC detection dataset, and establishing a new state-of-the-art (SOTA). Additionally, we validate the applicability and effectiveness of LQ-Adapter on the Kvasir-Seg dataset for polyp detection from colonoscopy images. Superior performance of our design on this problem as well showcases its capability to handle diverse medical imaging tasks across different datasets. Code is released at https://github.com/ChetanMadan/LQ-Adapter
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