While various deep learning methods were proposed for low-dose computed
tomography (CT) denoising, they often suffer from over-smoothing, blurring, and
lack of explainability. To alleviate these issues, we propose a plug-and-play
Language-Engaged Dual-space Alignment loss (LEDA) to optimize low-dose CT
denoising models. Our idea is to leverage large language models (LLMs) to align
denoised CT and normal dose CT images in both the continuous perceptual space
and discrete semantic space, which is the first LLM-based scheme for low-dose
CT denoising. LEDA involves two steps: the first is to pretrain an LLM-guided
CT autoencoder, which can encode a CT image into continuous high-level features
and quantize them into a token space to produce semantic tokens derived from
the LLM's vocabulary; and the second is to minimize the discrepancy between the
denoised CT images and normal dose CT in terms of both encoded high-level
features and quantized token embeddings derived by the LLM-guided CT
autoencoder. Extensive experimental results on two public LDCT denoising
datasets demonstrate that our LEDA can enhance existing denoising models in
terms of quantitative metrics and qualitative evaluation, and also provide
explainability through language-level image understanding. Source code is
available at this https URL

提出了一种基于大型语言模型的方案以减轻常见的 CT 图像低剂量去噪方法中的模糊、模糊和可解释性问题。该方案包括两个步骤：首先是对 CT 图像进行编码并将其量化为语义标记，然后通过最小化高级特征和量化标记之间的差异来优化去噪模型。实验证明该方案在定量指标和定性评估方面都能提高现有的去噪模型，并且通过语言级别的图像理解提供解释性。

低剂量 CT 噪声降低与语言双空间对齐

Low-dose CT Denoising with Language-engaged Dual-space Alignment

Existing distantly supervised relation extractors usually rely on noisy data
for both model training and evaluation, which may lead to
garbage-in-garbage-out systems. To alleviate the problem, we study whether a
small clean dataset could help improve the quality of distantly supervised
models. We show that besides getting a more convincing evaluation of models, a
small clean dataset also helps us to build more robust denoising models.
Specifically, we propose a new criterion for clean instance selection based on
influence functions. It collects sample-level evidence for recognizing good
instances (which is more informative than loss-level evidence). We also propose
a teacher-student mechanism for controlling purity of intermediate results when
bootstrapping the clean set. The whole approach is model-agnostic and
demonstrates strong performances on both denoising real (NYT) and synthetic
noisy datasets.

本文研究远程监督关系提取器的质量问题，旨在通过清洗少量数据以提高模型预测效果，并引入影响函数及师生机制来筛选优质实例和控制中间结果的纯度，取得了良好的去噪效果。

少量干净实例帮助去噪远程监督

Few Clean Instances Help Denoising Distant Supervision

Denoising diffusion probabilistic models (DDPMs) have shown impressive
results on sequence generation by iteratively corrupting each example and then
learning to map corrupted versions back to the original. However, previous work
has largely focused on in-place corruption, adding noise to each pixel or token
individually while keeping their locations the same. In this work, we consider
a broader class of corruption processes and denoising models over sequence data
that can insert and delete elements, while still being efficient to train and
sample from. We demonstrate that these models outperform standard in-place
models on an arithmetic sequence task, and that when trained on the text8
dataset they can be used to fix spelling errors without any fine-tuning.

本文提出了一种更广泛的 DDPMs，能够处理序列数据中插入和删除元素的噪声，该模型在算术序列任务和 text8 数据集上表现出较好的结果，并可用于拼写错误的修复。