Data driven models for automated diagnosis in radiology suffer from
insufficient and imbalanced datasets due to low representation of pathology in
a population and the cost of expert annotations. Datasets can be bolstered
through data augmentation. However, even when utilizing a full suite of
transformations during model training, typical data augmentations do not
address variations in human anatomy. An alternative direction is to synthesize
data using generative models, which can potentially craft datasets with
specific attributes. While this holds promise, commonly used generative models
such as Generative Adversarial Networks may inadvertently produce anatomically
inaccurate features. On the other hand, diffusion models, which offer greater
stability, tend to memorize training data, raising concerns about privacy and
generative diversity. Alternatively, inpainting has the potential to augment
data through directly inserting pathology in medical images. However, this
approach introduces a new challenge: accurately merging the generated
pathological features with the surrounding anatomical context. While inpainting
is a well established method for addressing simple lesions, its application to
pathologies that involve complex structural changes remains relatively
unexplored. We propose an efficient method for inpainting pathological features
onto healthy anatomy in MRI through voxelwise noise scheduling in a latent
diffusion model. We evaluate the method's ability to insert disc herniation and
central canal stenosis in lumbar spine sagittal T2 MRI, and it achieves
superior Frechet Inception Distance compared to state-of-the-art methods.

通过沿用高阶无噪音 LVGHD（三维）均一化成训练数据的分量，实现了数据的复制与修复，有助于预测分化格里马右半球的单元。

用潜在扩散在腰椎核磁共振图像中修复病理缺损

Inpainting Pathology in Lumbar Spine MRI with Latent Diffusion

Dysarthria speech contains the pathological characteristics of vocal tract
and vocal fold, but so far, they have not yet been included in traditional
acoustic feature sets. Moreover, the nonlinearity and non-stationarity of
speech have been ignored. In this paper, we propose a feature enhancement
algorithm for dysarthria speech called WHFEMD. It combines empirical mode
decomposition (EMD) and fast Walsh-Hadamard transform (FWHT) to enhance
features. With the proposed algorithm, the fast Fourier transform of the
dysarthria speech is first performed and then followed by EMD to get intrinsic
mode functions (IMFs). After that, FWHT is used to output new coefficients and
to extract statistical features based on IMFs, power spectral density, and
enhanced gammatone frequency cepstral coefficients. To evaluate the proposed
approach, we conducted experiments on two public pathological speech databases
including UA Speech and TORGO. The results show that our algorithm performed
better than traditional features in classification. We achieved improvements of
13.8% (UA Speech) and 3.84% (TORGO), respectively. Furthermore, the
incorporation of an imbalanced classification algorithm to address data
imbalance has resulted in a 12.18% increase in recognition accuracy. This
algorithm effectively addresses the challenges of the imbalanced dataset and
non-linearity in dysarthric speech and simultaneously provides a robust
representation of the local pathological features of the vocal folds and
tracts.

本文提出了一个用于改善发音障碍语音的特征增强算法，名为 WHFEMD。该算法结合了经验模态分解（EMD）和快速 Walsh-Hadamard 变换（FWHT），用于提取和增强特征，以及表示声带和声道的局部病理特征。在实验中，我们将该算法应用于两个公共病理语音数据库，并取得了显著的分类性能提升和识别准确率的增加。

利用经验模态分解和 Walsh-Hadamard 变换增强发音障碍语音特征表征

Enhancing dysarthria speech feature representation with empirical mode  decomposition and Walsh-Hadamard transform

Retinal disease is one of the primary causes of visual impairment, and early
diagnosis is essential for preventing further deterioration. Nowadays, many
works have explored Transformers for diagnosing diseases due to their strong
visual representation capabilities. However, retinal diseases exhibit milder
forms and often present with overlapping signs, which pose great difficulties
for accurate multi-class classification. Therefore, we propose a new framework
named Multi-Scale Patch Message Passing Swin Transformer for multi-class
retinal disease classification. Specifically, we design a Patch Message Passing
(PMP) module based on the Message Passing mechanism to establish global
interaction for pathological semantic features and to exploit the subtle
differences further between different diseases. Moreover, considering the
various scale of pathological features we integrate multiple PMP modules for
different patch sizes. For evaluation, we have constructed a new dataset, named
OPTOS dataset, consisting of 1,033 high-resolution fundus images photographed
by Optos camera and conducted comprehensive experiments to validate the
efficacy of our proposed method. And the results on both the public dataset and
our dataset demonstrate that our method achieves remarkable performance
compared to state-of-the-art methods.

提出了一种名为 Multi-Scale Patch Message Passing Swin Transformer 的新框架，用于多类视网膜疾病分类的精确诊断。该框架使用 Patch Message Passing（PMP）模块来建立病理语义特征的全局交互，并进一步挖掘不同疾病之间的细微差异，通过整合多个 PMP 模块来处理不同大小的病理特征。实验结果表明，该方法在公共数据集和自建数据集上相比最先进的方法具有显著的性能。