This research paper explores the classification of knee osteoarthritis (OA)
severity levels using advanced computer vision models and augmentation
techniques. The study investigates the effectiveness of data preprocessing,
including Contrast-Limited Adaptive Histogram Equalization (CLAHE), and data
augmentation using diffusion models. Three experiments were conducted: training
models on the original dataset, training models on the preprocessed dataset,
and training models on the augmented dataset. The results show that data
preprocessing and augmentation significantly improve the accuracy of the
models. The EfficientNetB3 model achieved the highest accuracy of 84\% on the
augmented dataset. Additionally, attention visualization techniques, such as
Grad-CAM, are utilized to provide detailed attention maps, enhancing the
understanding and trustworthiness of the models. These findings highlight the
potential of combining advanced models with augmented data and attention
visualization for accurate knee OA severity classification.

本研究探讨了使用先进的计算机视觉模型和增强技术对膝关节骨关节炎（OA）的严重程度进行分类。研究中调查了数据预处理，包括对比度受限的自适应直方图均衡化（CLAHE），以及使用扩散模型进行数据增强的效果。实验分为三个部分：在原始数据集上训练模型，在预处理数据集上训练模型，以及在增强数据集上训练模型。结果显示，数据预处理和数据增强显著提高了模型的准确性。EfficientNetB3 模型在增强数据集上达到了 84％的最高准确率。此外，还应用了注意力可视化技术，如 Grad-CAM，提供了详细的注意力图，增强了对模型的理解和可信度。这些发现突显了将先进模型与增强数据和注意力可视化相结合的潜力，从而实现准确的膝 OA 严重程度分类。

借助扩散增强图像提升膝关节骨关节炎严重程度分类

Enhancing Knee Osteoarthritis severity level classification using  diffusion augmented images

Dysarthria is a motor speech disorder often characterized by reduced speech
intelligibility through slow, uncoordinated control of speech production
muscles. Automatic Speech recognition (ASR) systems can help dysarthric talkers
communicate more effectively. However, robust dysarthria-specific ASR requires
a significant amount of training speech, which is not readily available for
dysarthric talkers. This paper presents a new dysarthric speech synthesis
method for the purpose of ASR training data augmentation. Differences in
prosodic and acoustic characteristics of dysarthric spontaneous speech at
varying severity levels are important components for dysarthric speech
modeling, synthesis, and augmentation. For dysarthric speech synthesis, a
modified neural multi-talker TTS is implemented by adding a dysarthria severity
level coefficient and a pause insertion model to synthesize dysarthric speech
for varying severity levels. To evaluate the effectiveness for synthesis of
training data for ASR, dysarthria-specific speech recognition was used. Results
show that a DNN-HMM model trained on additional synthetic dysarthric speech
achieves WER improvement of 12.2% compared to the baseline, and that the
addition of the severity level and pause insertion controls decrease WER by
6.5%, showing the effectiveness of adding these parameters. Overall results on
the TORGO database demonstrate that using dysarthric synthetic speech to
increase the amount of dysarthric-patterned speech for training has significant
impact on the dysarthric ASR systems. In addition, we have conducted a
subjective evaluation to evaluate the dysarthric-ness and similarity of
synthesized speech. Our subjective evaluation shows that the perceived
dysartrhic-ness of synthesized speech is similar to that of true dysarthric
speech, especially for higher levels of dysarthria

该研究提出了一种新的口吃性语音合成方法，用于自动语音识别 (ASR) 训练数据的增强，并通过主观评估证明了合成语音与真实口吃性语音在口吃感和相似性方面的相似性，特别是对于严重口吃者。

为 ASR 数据增强精确合成口吃病患者语音

Accurate synthesis of Dysarthric Speech for ASR data augmentation

The speech act of complaining is used by humans to communicate a negative
mismatch between reality and expectations as a reaction to an unfavorable
situation. Linguistic theory of pragmatics categorizes complaints into various
severity levels based on the face-threat that the complainer is willing to
undertake. This is particularly useful for understanding the intent of
complainers and how humans develop suitable apology strategies. In this paper,
we study the severity level of complaints for the first time in computational
linguistics. To facilitate this, we enrich a publicly available data set of
complaints with four severity categories and train different transformer-based
networks combined with linguistic information achieving 55.7 macro F1. We also
jointly model binary complaint classification and complaint severity in a
multi-task setting achieving new state-of-the-art results on binary complaint
detection reaching up to 88.2 macro F1. Finally, we present a qualitative
analysis of the behavior of our models in predicting complaint severity levels.

本篇论文是第一次在计算语言学领域研究投诉的严重程度等级，在公开数据集中加入四个严重程度等级并采用不同的基于 transformer 的网络结构和语言信息来训练模型，在二进制投诉检测和投诉严重程度等级多任务设置的联合模型中达到了最新的最优结果，最后提供了模型预测投诉严重等级的定性分析。