The BReAst Carcinoma Subtyping (BRACS) is a new dataset of hematoxylin and eosin (H&E) histopathological images of breast carcinoma. BRACS has been built on the basis of an Agreement between IRCCS Fondazione Pascale, Institute for High Performance Computing and Networking (ICAR) of National Research Council (CNR), and IBM Research-Zurich for the "Development of methodologies and tools for the identification of atypical tumors in breast cancer pathology through the automatic analysis of histological images". This dataset offers a platform for researchers to compare strategies and algorithms for automated detection/classification of breast tumors in H&E stained tissue samples collected by mastectomy or biopsy. BRACS differs from most of the public breast cancer image datasets since it includes images representing atypical lesions. An early diagnosis of these atypical lesions could prevent the worsening into malignant cancer. In details, BRACS contains images characterized by the following kind of lesions: Pathological Benign (PB), Usual Ductal Hyperplasia (UDH), Flat Epithelial Atypia (FEA), Atypical Ductal Hyperplasia (ADH), Ductal Carcinoma in Situ (DCIS) and Invasive Carcinoma (IC). Also images representing Normal (N) tissue samples, i.e. glandular tissue samples without lesions, are included into BRACS.
BRACS: BReAst Carcinoma Subtyping
Brancati N;De Pietro G;Frucci M;Riccio D;
2021
Abstract
The BReAst Carcinoma Subtyping (BRACS) is a new dataset of hematoxylin and eosin (H&E) histopathological images of breast carcinoma. BRACS has been built on the basis of an Agreement between IRCCS Fondazione Pascale, Institute for High Performance Computing and Networking (ICAR) of National Research Council (CNR), and IBM Research-Zurich for the "Development of methodologies and tools for the identification of atypical tumors in breast cancer pathology through the automatic analysis of histological images". This dataset offers a platform for researchers to compare strategies and algorithms for automated detection/classification of breast tumors in H&E stained tissue samples collected by mastectomy or biopsy. BRACS differs from most of the public breast cancer image datasets since it includes images representing atypical lesions. An early diagnosis of these atypical lesions could prevent the worsening into malignant cancer. In details, BRACS contains images characterized by the following kind of lesions: Pathological Benign (PB), Usual Ductal Hyperplasia (UDH), Flat Epithelial Atypia (FEA), Atypical Ductal Hyperplasia (ADH), Ductal Carcinoma in Situ (DCIS) and Invasive Carcinoma (IC). Also images representing Normal (N) tissue samples, i.e. glandular tissue samples without lesions, are included into BRACS.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
