Efficient and precise quantification of lymphocytes in histopathology slides
is imperative for the characterization of the tumor microenvironment and
immunotherapy response insights. We developed a data-centric optimization
pipeline that attain great lymphocyte detection performance using an
off-the-shelf YOLOv5 model, without any architectural modifications. Our
contribution that rely on strategic dataset augmentation strategies, includes
novel biological upsampling and custom visual cohesion transformations tailored
to the unique properties of tissue imagery, and enables to dramatically improve
model performances. Our optimization reveals a pivotal realization: given
intensive customization, standard computational pathology models can achieve
high-capability biomarker development, without increasing the architectural
complexity. We showcase the interest of this approach in the context of breast
cancer where our strategies lead to good lymphocyte detection performances,
echoing a broadly impactful paradigm shift. Furthermore, our data curation
techniques enable crucial histological analysis benchmarks, highlighting
improved generalizable potential.

淋巴细胞在组织病理学切片中的高效、准确量化对于肿瘤微环境和免疫治疗反应的表征至关重要。我们开发了一个基于数据的优化流程，利用现成的 YOLOv5 模型在不进行任何结构修改的情况下实现了出色的淋巴细胞检测性能。我们的工作依赖于策略性的数据集增强策略，包括新颖的生物上采样和定制的视觉连贯转换，针对组织图像的独特特性进行设计，从而大大提高了模型的性能。我们的优化揭示了一个关键的实现：在进行了密集定制化之后，标准的计算病理学模型可以实现高能力的生物标志物开发，而不增加架构复杂性。我们展示了这种方法在乳腺癌背景下的兴趣，证明我们的策略可以实现良好的淋巴细胞检测性能，引发了广泛的影响性的范式转变。此外，我们的数据整理技术提供了关键的组织学分析基准，突显了提高的通用潜力。

通过数据中心策略优化乳腺癌全切片图像中的淋巴细胞检测

Optimizing Lymphocyte Detection in Breast Cancer Whole Slide Imaging  through Data-Centric Strategies

Transformers, due to their ability to learn long range dependencies, have
overcome the shortcomings of convolutional neural networks (CNNs) for global
perspective learning. Therefore, they have gained the focus of researchers for
several vision related tasks including medical diagnosis. However, their
multi-head attention module only captures global level feature representations,
which is insufficient for medical images. To address this issue, we propose a
Channel Boosted Hybrid Vision Transformer (CB HVT) that uses transfer learning
to generate boosted channels and employs both transformers and CNNs to analyse
lymphocytes in histopathological images. The proposed CB HVT comprises five
modules, including a channel generation module, channel exploitation module,
channel merging module, region-aware module, and a detection and segmentation
head, which work together to effectively identify lymphocytes. The channel
generation module uses the idea of channel boosting through transfer learning
to extract diverse channels from different auxiliary learners. In the CB HVT,
these boosted channels are first concatenated and ranked using an attention
mechanism in the channel exploitation module. A fusion block is then utilized
in the channel merging module for a gradual and systematic merging of the
diverse boosted channels to improve the network's learning representations. The
CB HVT also employs a proposal network in its region aware module and a head to
effectively identify objects, even in overlapping regions and with artifacts.
We evaluated the proposed CB HVT on two publicly available datasets for
lymphocyte assessment in histopathological images. The results show that CB HVT
outperformed other state of the art detection models, and has good
generalization ability, demonstrating its value as a tool for pathologists.

CB HVT 是一种具有先进性能的医学图像诊断工具，利用了 transformers 和 CNN，采用通道增强方式来进行 AI 辅助病理检测和分析，能够有效识别组织图像中的淋巴细胞等相关信息。