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

We describe a completely automated large scale visual recommendation system
for fashion. Our focus is to efficiently harness the availability of large
quantities of online fashion images and their rich meta-data. Specifically, we
propose four data driven models in the form of Complementary Nearest Neighbor
Consensus, Gaussian Mixture Models, Texture Agnostic Retrieval and Markov Chain
LDA for solving this problem. We analyze relative merits and pitfalls of these
algorithms through extensive experimentation on a large-scale data set and
baseline them against existing ideas from color science. We also illustrate key
fashion insights learned through these experiments and show how they can be
employed to design better recommendation systems. Finally, we also outline a
large-scale annotated data set of fashion images (Fashion-136K) that can be
exploited for future vision research.

本文提出了四个数据驱动模型用于大规模的视觉推荐系统，这些模型可以有效地利用大量的时尚图片及其丰富的元数据，通过对大规模数据集的深入实验对这些算法进行了分析，并在颜色科学的基础上对其进行了基线，同时展示了从这些实验中学到的关键时尚见解并介绍了一个可以用于未来视觉研究的大规模时尚图像注释数据集（Fashion-136K）。