GPC：生成式通用病理图像分类器
GPC: Generative and General Pathology Image Classifier
AI总结：
针对病理图像分类需为每个任务构建专属模型、迁移困难的问题，本文提出任务无关的生成式通用分类器GPC，结合卷积神经网络与Transformer语言模型实现图像到文本分类，在多任务多数据集上验证了构建通用病理图像分析模型的潜力。
摘要：
深度学习已越来越多地被应用于各类计算病理任务中，以提升效率、准确性和鲁棒性。尽管取得了成功，但以往大多数图像分类方法存在关键缺陷。病理领域存在大量任务，但研究人员需要为每个任务单独构建模型，即任务专属模型，这导致模型数量、训练资源和成本增加。此外，将任意任务专属模型迁移到其他任务仍是一个具有挑战性的问题。本文提出了一种任务无关的生成式通用病理图像分类器，名为GPC，旨在从多种病理图像中学习，并在一个统一模型中完成大量分类任务。GPC配备卷积神经网络和基于Transformer的语言模型，通过图像到文本的分类机制将病理图像映射到高维特征空间，并生成相关的文本类别标签。我们在四个不同病理图像分类任务的六个数据集上对GPC进行了评估。实验结果表明，GPC在开发高效且有效的病理图像分析通用模型方面具有相当大的潜力。
英文摘要：
Deep learning has been increasingly incorporated into various computational pathology applications to improve its efficiency, accuracy, and robustness. Although successful, most previous approaches for image classification have crucial drawbacks. There exist numerous tasks in pathology, but one needs to build a model per task, i.e., a task-specific model, thereby increasing the number of models, training resources, and cost. Moreover, transferring arbitrary task-specific model to another task is still a challenging problem. Herein, we propose a task-agnostic generative and general pathology image classifier, so called GPC, that aims at learning from diverse kinds of pathology images and conducting numerous classification tasks in a unified model. GPC, equipped with a convolutional neural network and a Transformer-based language model, maps pathology images into a high-dimensional feature space and generates pertinent class labels as texts via the image-to-text classification mechanism. We evaluate GPC on six datasets for four different pathology image classification tasks. Experimental results show that GPC holds considerable potential for developing an effective and efficient universal model for pathology image analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
