Pathology diagnosis based on EEG signals and decoding brain activity holds
immense importance in understanding neurological disorders. With the
advancement of artificial intelligence methods and machine learning techniques,
the potential for accurate data-driven diagnoses and effective treatments has
grown significantly. However, applying machine learning algorithms to
real-world datasets presents diverse challenges at multiple levels. The
scarcity of labelled data, especially in low regime scenarios with limited
availability of real patient cohorts due to high costs of recruitment,
underscores the vital deployment of scaling and transfer learning techniques.
In this study, we explore a real-world pathology classification task to
highlight the effectiveness of data and model scaling and cross-dataset
knowledge transfer. As such, we observe varying performance improvements
through data scaling, indicating the need for careful evaluation and labelling.
Additionally, we identify the challenges of possible negative transfer and
emphasize the significance of some key components to overcome distribution
shifts and potential spurious correlations and achieve positive transfer. We
see improvement in the performance of the target model on the target (NMT)
datasets by using the knowledge from the source dataset (TUAB) when a low
amount of labelled data was available. Our findings indicate a small and
generic model (e.g. ShallowNet) performs well on a single dataset, however, a
larger model (e.g. TCN) performs better on transfer and learning from a larger
and diverse dataset.

基于脑电图信号和解码脑活动的病理诊断在理解神经系统疾病方面具有巨大重要性；随着人工智能方法和机器学习技术的发展，准确的数据驱动诊断和有效的治疗潜力显著增长。然而，将机器学习算法应用于现实世界的数据集在多个层面上都面临各种挑战；特别是在低范围的场景中数据标记稀缺，由于征募费用高昂，真实患者群体的可用性有限，一直强调了规模化和迁移学习技术的重要部署。本研究探讨了一个真实的病理分类任务，以突出数据和模型规模化以及跨数据集知识迁移的有效性；观察到通过数据规模化而产生的性能改进程度不同，表明需要进行谨慎评估和标注；此外，我们还确定了可能的负迁移的挑战，并强调了克服分布迁移和潜在虚假相关性的一些关键组成部分的重要性，实现积极的迁移；当已有少量标记数据时，通过使用源数据集的知识（如 TUAB 数据集），我们发现目标模型在目标（NMT）数据集上表现得更好；我们的发现表明，一个小型和通用的模型（如 ShallowNet）在单个数据集上表现良好，然而，一个较大的模型（如 TCN）在从较大和多样化的数据集中进行迁移和学习时表现更好。

通过跨数据集迁移学习增强 EEG 信号通路中的病理检测

Amplifying Pathological Detection in EEG Signaling Pathways through  Cross-Dataset Transfer Learning

As advances in large language models (LLMs) and multimodal techniques
continue to mature, the development of general-purpose multimodal large
language models (MLLMs) has surged, with significant applications in natural
image interpretation. However, the field of pathology has largely remained
untapped in this regard, despite the growing need for accurate, timely, and
personalized diagnostics. To bridge the gap in pathology MLLMs, we present the
PathAsst in this study, which is a generative foundation AI assistant to
revolutionize diagnostic and predictive analytics in pathology. To develop
PathAsst, we collect over 142K high-quality pathology image-text pairs from a
variety of reliable sources, including PubMed, comprehensive pathology
textbooks, reputable pathology websites, and private data annotated by
pathologists. Leveraging the advanced capabilities of ChatGPT/GPT-4, we
generate over 180K instruction-following samples. Furthermore, we devise
additional instruction-following data, specifically tailored for the invocation
of the pathology-specific models, allowing the PathAsst to effectively interact
with these models based on the input image and user intent, consequently
enhancing the model's diagnostic capabilities. Subsequently, our PathAsst is
trained based on Vicuna-13B language model in coordination with the CLIP vision
encoder. The results of PathAsst show the potential of harnessing the
AI-powered generative foundation model to improve pathology diagnosis and
treatment processes. We are committed to open-sourcing our meticulously curated
dataset, as well as a comprehensive toolkit designed to aid researchers in the
extensive collection and preprocessing of their own datasets. Resources can be
obtained at
this https URL

本文提出了 PathAsst，一种生成式 AI 助手，利用了 ChatGPT/GPT-4 和 Vicuna-13B 语言模型与 CLIP 视觉编码器，对 142K 高质量病理图像文本对进行了训练。结果表明，利用这种 AI 模型可以改善病理诊断和治疗过程。

PathAsst：基于生成式基础人工智能的病理学辅助系统的重新定义

PathAsst: Redefining Pathology through Generative Foundation AI  Assistant for Pathology

Breast cancer is the most commonly diagnosed cancer and registers the highest
number of deaths for women with cancer. Recent advancements in diagnostic
activities combined with large-scale screening policies have significantly
lowered the mortality rates for breast cancer patients. However, the manual
inspection of tissue slides by the pathologists is cumbersome, time-consuming,
and is subject to significant inter- and intra-observer variability. Recently,
the advent of whole-slide scanning systems have empowered the rapid
digitization of pathology slides, and enabled to develop digital workflows.
These advances further enable to leverage Artificial Intelligence (AI) to
assist, automate, and augment pathological diagnosis. But the AI techniques,
especially Deep Learning (DL), require a large amount of high-quality annotated
data to learn from. Constructing such task-specific datasets poses several
challenges, such as, data-acquisition level constrains, time-consuming and
expensive annotations, and anonymization of private information. In this paper,
we introduce the BReAst Carcinoma Subtyping (BRACS) dataset, a large cohort of
annotated Hematoxylin & Eosin (H&E)-stained images to facilitate the
characterization of breast lesions. BRACS contains 547 Whole-Slide Images
(WSIs), and 4539 Regions of Interest (ROIs) extracted from the WSIs. Each WSI,
and respective ROIs, are annotated by the consensus of three board-certified
pathologists into different lesion categories. Specifically, BRACS includes
three lesion types, i.e., benign, malignant and atypical, which are further
subtyped into seven categories. It is, to the best of our knowledge, the
largest annotated dataset for breast cancer subtyping both at WSI- and
ROI-level. Further, by including the understudied atypical lesions, BRACS
offers an unique opportunity for leveraging AI to better understand their
characteristics.

本文介绍了 BReAst Carcinoma Subtyping（BRACS）数据集，这是一个包含 547 张 WSI 和 4539 个感兴趣区域（ROIs）的大型数据集，可以帮助进一步了解乳腺癌的特征以及利用人工智能技术对其进行诊断和分类。