Breast cancer is the most common invasive cancer in women, affecting more
than 10% of women worldwide. Microscopic analysis of a biopsy remains one of
the most important methods to diagnose the type of breast cancer. This requires
specialized analysis by pathologists, in a task that i) is highly time- and
cost-consuming and ii) often leads to nonconsensual results. The relevance and
potential of automatic classification algorithms using hematoxylin-eosin
stained histopathological images has already been demonstrated, but the
reported results are still sub-optimal for clinical use. With the goal of
advancing the state-of-the-art in automatic classification, the Grand Challenge
on BreAst Cancer Histology images (BACH) was organized in conjunction with the
15th International Conference on Image Analysis and Recognition (ICIAR 2018). A
large annotated dataset, composed of both microscopy and whole-slide images,
was specifically compiled and made publicly available for the BACH challenge.
Following a positive response from the scientific community, a total of 64
submissions, out of 677 registrations, effectively entered the competition.
From the submitted algorithms it was possible to push forward the
state-of-the-art in terms of accuracy (87%) in automatic classification of
breast cancer with histopathological images. Convolutional neuronal networks
were the most successful methodology in the BACH challenge. Detailed analysis
of the collective results allowed the identification of remaining challenges in
the field and recommendations for future developments. The BACH dataset remains
publically available as to promote further improvements to the field of
automatic classification in digital pathology.

利用组织学图像的自动分类方法可以提高乳腺癌的诊断准确性，并通过组织学图像的自动分类的算法进一步推进形态学领域的发展。本文评估了在大型数据集上集成卷积神经网络的方法，将其用于在乳腺癌图片的分类问题，并在自动分类的准确性方面取得了一定的进展。