标题： eNCApsulate: 用于胶囊内窥镜精确诊断的NCA 显示英文标题

标题： eNCApsulate: NCA for Precision Diagnosis on Capsule Endoscopes

摘要： 无线胶囊内窥镜是一种用于整个胃肠道的非侵入性成像方法，是传统内窥镜无痛替代方案。 它生成了大量视频数据，需要大量审查时间，并且吞咽后定位胶囊是一个挑战。 出血检测和深度估计等技术可以帮助病灶定位，但深度学习模型通常太大，无法直接在胶囊上运行。 针对出血分割和深度估计的神经细胞自动机（NCA）是在胶囊内窥镜图像上训练的。 对于单目深度估计，我们通过将基础模型的输出作为伪地面实况来蒸馏一个大型基础模型到精简的NCA架构中。 然后我们将训练好的NCA移植到ESP32微控制器上，在硬件上实现高效的图像处理，小到相机胶囊。 与其它便携式分割模型相比，NCA更准确（Dice），同时比其他小型模型存储在内存中的参数少100倍以上。 NCA深度估计的视觉结果看起来令人信服，在某些情况下甚至超过了伪地面实况的真实性与细节。 在ESP32-S3上的运行时优化显著加速了平均推理速度，超过3倍。 通过几项算法调整和蒸馏，可以将NCA模型封装到适合无线胶囊内窥镜的微控制器中。 这是首个能够在微型设备上实现可靠出血分割和深度估计的工作，为结合视觉里程计作为胶囊精确定位手段的精确诊断铺平了道路。 显示英文摘要

摘要： Wireless Capsule Endoscopy is a non-invasive imaging method for the entire gastrointestinal tract, and is a pain-free alternative to traditional endoscopy. It generates extensive video data that requires significant review time, and localizing the capsule after ingestion is a challenge. Techniques like bleeding detection and depth estimation can help with localization of pathologies, but deep learning models are typically too large to run directly on the capsule. Neural Cellular Automata (NCA) for bleeding segmentation and depth estimation are trained on capsule endoscopic images. For monocular depth estimation, we distill a large foundation model into the lean NCA architecture, by treating the outputs of the foundation model as pseudo ground truth. We then port the trained NCA to the ESP32 microcontroller, enabling efficient image processing on hardware as small as a camera capsule. NCA are more accurate (Dice) than other portable segmentation models, while requiring more than 100x fewer parameters stored in memory than other small-scale models. The visual results of NCA depth estimation look convincing, and in some cases beat the realism and detail of the pseudo ground truth. Runtime optimizations on the ESP32-S3 accelerate the average inference speed significantly, by more than factor 3. With several algorithmic adjustments and distillation, it is possible to eNCApsulate NCA models into microcontrollers that fit into wireless capsule endoscopes. This is the first work that enables reliable bleeding segmentation and depth estimation on a miniaturized device, paving the way for precise diagnosis combined with visual odometry as a means of precise localization of the capsule -- on the capsule.