The BraTS 2021 challenge celebrates its 10th anniversary and is jointly
organized by the Radiological Society of North America (RSNA), the American
Society of Neuroradiology (ASNR), and the Medical Image Computing and Computer
Assisted Interventions (MICCAI) society. Since its inception, BraTS has been
focusing on being a common benchmarking venue for brain glioma segmentation
algorithms, with well-curated multi-institutional multi-parametric magnetic
resonance imaging (mpMRI) data. Gliomas are the most common primary
malignancies of the central nervous system, with varying degrees of
aggressiveness and prognosis. The RSNA-ASNR-MICCAI BraTS 2021 challenge targets
the evaluation of computational algorithms assessing the same tumor
compartmentalization, as well as the underlying tumor's molecular
characterization, in pre-operative baseline mpMRI data from 2,040 patients.
Specifically, the two tasks that BraTS 2021 focuses on are: a) the segmentation
of the histologically distinct brain tumor sub-regions, and b) the
classification of the tumor's O[6]-methylguanine-DNA methyltransferase (MGMT)
promoter methylation status. The performance evaluation of all participating
algorithms in BraTS 2021 will be conducted through the Sage Bionetworks Synapse
platform (Task 1) and Kaggle (Task 2), concluding in distributing to the top
ranked participants monetary awards of $60,000 collectively.

BraTS 2021 challenge focuses on benchmarking brain glioma segmentation algorithms and tumor classification based on mpMRI data from 2,040 patients, with performance evaluation on Sage Bionetworks Synapse platform and Kaggle, and awarding top participants $60,000.

RSNA-ASNR-MICCAI BraTS 2021 脑肿瘤分割和放射基因分类基准测试

The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation  and Radiogenomic Classification

Domain shift is a significant problem in histopathology. There can be large
differences in data characteristics of whole-slide images between medical
centers and scanners, making generalization of deep learning to unseen data
difficult. To gain a better understanding of the problem, we present a study on
convolutional neural networks trained for tumor classification of H&E stained
whole-slide images. We analyze how augmentation and normalization strategies
affect performance and learned representations, and what features a trained
model respond to. Most centrally, we present a novel measure for evaluating the
distance between domains in the context of the learned representation of a
particular model. This measure can reveal how sensitive a model is to domain
variations, and can be used to detect new data that a model will have problems
generalizing to. The results show how learning is heavily influenced by the
preparation of training data, and that the latent representation used to do
classification is sensitive to changes in data distribution, especially when
training without augmentation or normalization.

该论文研究了卷积神经网络在组织病理学中用于肿瘤分类的应用，分析了数据增强和归一化对模型性能和表示学习的影响，提出了衡量模型领域差异距离的新方法，结果表明训练数据的准备对学习结果有很大影响。

深度学习在组织病理学中的领域转移研究

A Closer Look at Domain Shift for Deep Learning in Histopathology

Performance of data-driven network for tumor classification varies with
stain-style of histopathological images. This article proposes the stain-style
transfer (SST) model based on conditional generative adversarial networks
(GANs) which is to learn not only the certain color distribution but also the
corresponding histopathological pattern. Our model considers feature-preserving
loss in addition to well-known GAN loss. Consequently our model does not only
transfers initial stain-styles to the desired one but also prevent the
degradation of tumor classifier on transferred images. The model is examined
using the CAMELYON16 dataset.

本文提出了一种基于条件生成对抗网络（GANs）的染色风格转换（SST）模型，用于学习染色风格和对应的组织病理图像，并考虑了特征保持损失，以避免肿瘤分类器在转移图像时降级，研究表明该模型基于 CAMELYON16 数据集的性能因组织病理样本的染色风格而变化。