Wireless Capsule Endoscopy (WCE) is highly valued for its non-invasive and
painless approach, though its effectiveness is compromised by uneven
illumination from hardware constraints and complex internal dynamics, leading
to overexposed or underexposed images. While researchers have discussed the
challenges of low-light enhancement in WCE, the issue of correcting for
different exposure levels remains underexplored. To tackle this, we introduce
EndoUIC, a WCE unified illumination correction solution using an end-to-end
promptable diffusion transformer (DFT) model. In our work, the illumination
prompt module shall navigate the model to adapt to different exposure levels
and perform targeted image enhancement, in which the Adaptive Prompt
Integration (API) and Global Prompt Scanner (GPS) modules shall further boost
the concurrent representation learning between the prompt parameters and
features. Besides, the U-shaped restoration DFT model shall capture the
long-range dependencies and contextual information for unified illumination
restoration. Moreover, we present a novel Capsule-endoscopy Exposure Correction
(CEC) dataset, including ground-truth and corrupted image pairs annotated by
expert photographers. Extensive experiments against a variety of
state-of-the-art (SOTA) methods on four datasets showcase the effectiveness of
our proposed method and components in WCE illumination restoration, and the
additional downstream experiments further demonstrate its utility for clinical
diagnosis and surgical assistance.

Wireless Capsule Endoscopy (WCE) 采用一种无创痛苦的方法被高度重视，尽管硬件限制和复杂的内部动态导致了不均匀的照明，从而导致过曝或欠曝图像。我们引入了 EndoUIC，一种使用端到端可提示的扩散变换器（DFT）模型的 WCE 统一照明校正解决方案，以解决这个问题。我们的工作中，照明提示模块将导航模型以适应不同的曝光水平并执行有针对性的图像增强，在其中自适应提示集成（API）和全局提示扫描器（GPS）模块将进一步增强提示参数和特征之间的并行表示学习。此外，U 形恢复 DFT 模型将捕捉统一照明恢复的长程依赖和上下文信息。此外，我们提供了一个新颖的胶囊内镜暴露校正（CEC）数据集，包括由专业摄影师注释的地面真实图像和损坏图像对。对四个数据集上的各种先进方法进行的大量实验证明了我们提出的方法和组件在 WCE 照明恢复中的有效性，额外的下游实验进一步展示了其在临床诊断和手术辅助中的实用性。

EndoUIC: 可提示的扩散变压器在胶囊内窥镜中的统一照明校正

EndoUIC: Promptable Diffusion Transformer for Unified Illumination  Correction in Capsule Endoscopy

Accurate disease categorization using endoscopic images is a significant
problem in Gastroenterology. This paper describes a technique for assisting
medical diagnosis procedures and identifying gastrointestinal tract disorders
based on the categorization of characteristics taken from endoscopic pictures
using a vision transformer and transfer learning model. Vision transformer has
shown very promising results on difficult image classification tasks. In this
paper, we have suggested a vision transformer based approach to detect
gastrointestianl diseases from wireless capsule endoscopy (WCE) curated images
of colon with an accuracy of 95.63\%. We have compared this transformer based
approach with pretrained convolutional neural network (CNN) model DenseNet201
and demonstrated that vision transformer surpassed DenseNet201 in various
quantitative performance evaluation metrics.

本研究使用视觉转换器和迁移学习模型对内窥镜图像进行特征分类，从而协助医疗诊断，识别肠道疾病，准确率可达 95.63％。与预训练的卷积神经网络模型 DenseNet201 进行比较，并证明视觉转换器在各种数量化性能评估指标上均优于 DenseNet201。