Universidade do Vale do Rio dos Sinos (UNISINOS), Av. Unisinos, 950 - Cristo Rei, São Leopoldo, 93022-750, RS, Brazil; Instituto Federal de Educação, Ciência e Tecnologia de Mato Grosso (IFMT), Rua, Zulmira Canavarros, 95 - Centro Cuiabá, 78605-000, MT, Brazil; Corresponding author.
Rafael Kunst
Universidade do Vale do Rio dos Sinos (UNISINOS), Av. Unisinos, 950 - Cristo Rei, São Leopoldo, 93022-750, RS, Brazil
Jorge Luis Victoria Barbosa
Universidade do Vale do Rio dos Sinos (UNISINOS), Av. Unisinos, 950 - Cristo Rei, São Leopoldo, 93022-750, RS, Brazil
Ana Paula Thiesen Leindecker
Universidade Federal do Rio Grande do Sul / Porgrama de Pós-graduação em Medicina: Ciências Cirúrgicas, Rua Ramiro Barcelos 2300 - Santa Cecilia Porto Alegre, 90035-007, RS, Brazil
Ricardo F. Savaris
Universidade Federal do Rio Grande do Sul / Porgrama de Pós-graduação em Medicina: Ciências Cirúrgicas, Rua Ramiro Barcelos 2300 - Santa Cecilia Porto Alegre, 90035-007, RS, Brazil
This dataset is composed of photomicrographs of the immunohistochemical expression of Biglycan (BGN) in breast tissue, with and without cancer, using only the staining of 3-3′ diaminobenzidine (DAB), after processing images with color deconvolution plugin, from Image J. The immunohistochemical DAB expression of BGN was obtained using the monoclonal antibody (M01) (clone 4E1-1G7 - Abnova Corporation, mouse anti-human). Photomicrographs were obtained, under standard conditions, using an optical microscope, with UPlanFI 100x objective (resolution: 2.75 mm), yielding an image size of 4800 × 3600 pixels. After color deconvolution, the dataset with 336 images was divided into 2 two categories: (I) with cancer and (II) without cancer. This dataset allows the training and validation of machine learning models to diagnose, recognize and classify the presence of breast cancer, using the intensity of the colors of the BGN.
