Even though novel imaging techniques have been successful in studying brain
structure and function, the measured biological signals are often contaminated
by multiple sources of noise, arising due to e.g. head movements of the
individual being scanned, limited spatial/temporal resolution, or other issues
specific to each imaging technology. Data preprocessing (e.g. denoising) is
therefore critical. Preprocessing pipelines have become increasingly complex
over the years, but also more flexible, and this flexibility can have a
significant impact on the final results and conclusions of a given study. This
large parameter space is often referred to as multiverse analyses. Here, we
provide conceptual and practical tools for statistical analyses that can
aggregate multiple pipeline results along with a new sensitivity analysis
testing for hypotheses across pipelines such as "no effect across all
pipelines" or "at least one pipeline with no effect". The proposed framework is
generic and can be applied to any multiverse scenario, but we illustrate its
use based on positron emission tomography data.

通过对大量正电子发射断层扫描数据进行统计分析，我们提出了一种通用性框架，用于处理脑结构和功能的多元宇宙情景中的噪音，并汇总多个数据预处理流程的结果，以得出准确结论。

神经影像前处理策略对后续统计分析的影响的敏感性分析

A sensitivity analysis to quantify the impact of neuroimaging  preprocessing strategies on subsequent statistical analyses

Palimpsests refer to historical manuscripts where erased writings have been
partially covered by the superimposition of a second writing. By employing
imaging techniques, e.g., multispectral imaging, it becomes possible to
identify features that are imperceptible to the naked eye, including faded and
erased inks. When dealing with overlapping inks, Artificial Intelligence
techniques can be utilized to disentangle complex nodes of overlapping letters.
In this work, we propose deep learning-based semantic segmentation as a method
for identifying and segmenting individual letters in overlapping characters.
The experiment was conceived as a proof of concept, focusing on the palimpsests
of the Ars Grammatica by Prisciano as a case study. Furthermore, caveats and
prospects of our approach combined with multispectral imaging are also
discussed.

通过图像处理技术识别被超级重印覆盖的历史手稿中的已擦除用墨，并借助人工智能技术解开重叠字母的复杂状况，本研究提出了基于深度学习的语义分割方法。以普里斯克里亚努斯的《语法艺术》为案例研究，本文探讨了该方法结合多光谱成像的预期与限制。

叠字文献中重叠字符的语义分割的深度学习实验

A deep learning experiment for semantic segmentation of overlapping  characters in palimpsests

The classification of the origin of blood clots is a crucial step in
diagnosing and treating ischemic stroke. Various imaging techniques such as
computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound have
been employed to detect and locate blood clots within the body. However,
identifying the origin of a blood clot remains challenging due to the
complexity of the blood flow dynamics and the limitations of the imaging
techniques. The study suggests a novel methodology for classifying the source
of a blood clot through the integration of data from whole-slide digital
pathology images, which are utilized to fine-tune several cutting-edge computer
vision models. Upon comparison, the SwinTransformerV2 model outperforms all the
other models and achieves an accuracy score of 94.24%, precision score of
94.41%, recall score of 94.09%, and, f1-score of 94.06%. Our approach shows
promising results in detecting the origin of blood clots in different vascular
regions and can potentially improve the diagnosis and management of ischemic
stroke.

研究提出通过整合全切片数字病理图像的数据，利用多种先进的计算机视觉模型对血栓来源进行分类的新方法，其中 SwinTransformerV2 模型表现最佳，可以在不同血管区域检测血栓的来源，有望改善缺血性卒中的诊断和治疗。