BACH: Grand challenge on breast cancer histology images

By Guilherme Aresta, Teresa Araújo, S. Kwok, Sai Saketh Chennamsetty, P. MohammedSafwanK., Alex Varghese, B. Marami, Marcel Prastawa, Monica Chan, M. Donovan and 26 more. Medical Image Anal., 2018

716 citations recorded by the source corpus.

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). BACH aimed at the classification and localization of clinically relevant histopathological classes in microscopy and whole-slide images from a large annotated dataset, specifically compiled and made publicly available for the challenge. Following a positive response from the scientific community, a total of 64 submissions, out of 677 registrations, effectively entered the competition. The submitted algorithms improved the state-of-the-art in automatic classification of breast cancer with microscopy images to an accuracy of 87%. 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 publicly available as to promote further improvements to the field of automatic classification in digital pathology.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/4831391ba622479b2f7345b792e8a301890887fe

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Citations in the corpus

Metrics Reloaded: Recommendations for image analysis validation (2022, neutral): “(3) Labeling of invasive/ noninvasive/ benign lesions on breast Whole Slide Imaging (WSI) [3].”
Breast cancer histopathological image classification using a hybrid deep neural network. (2020, methodology): “Recently, several excellent CNN-based methods for automatic and precise classification of breast cancer pathological images were developed for the ICIAR2018 challenge [20].”
Deep Learning for Whole Slide Image Analysis: An Overview (2019, methodology): “Automating this process through the use of convolutional neural networks (CNNs) has been shown to result in more discriminative features tailored for the problem at hand [4, 1, 38, 46].”
Self supervised contrastive learning for digital histopathology (2020, methodology): “For segmentation, we used the following three datasets: BACH challenge dataset (Aresta et al., 2019), Gleason2019 (Nir et al., 2018), and DigestPath2019 (Li et al., 2019).”
The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis (2020, background): “In the last few years, several challenges have been proposed in the field of digital pathology [26,27].”
Artificial intelligence for digital and computational pathology (2023, background): “…detection of breast metastasis in lymph nodes (CAMELYON16 and CAMELYON17) 5,227 , Gleason grading of prostate biopsies (PANDA) 3 , breast cancer subtyping (BACH) 228 , pan-cancer nucleus segmentation and classification (MoNuSac) 229 and gland segmentation in colon images (GlaS) 73 , among others.”