Reproducibility metadata and experimental artifacts from a prostate cancer binary segmentation study using the public DIAGSET database

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Nunes, Bruno Henrique; Silva, Ana Estela Antunes da, 2026, "Reproducibility metadata and experimental artifacts from a prostate cancer binary segmentation study using the public DIAGSET database", https://doi.org/10.25824/redu/EDPCF2, Repositório de Dados de Pesquisa da Unicamp , V1
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Description
This dataset contains reproducibility metadata, provenance records, configuration files, manifests, intermediate artifacts, and experimental results from a computational study on automated prostate cancer identification and binary segmentation in histopathological slide images. The study used the public DIAGSET database as the input image source; the original DIAGSET images are not redistributed in this deposit. The deposited materials document how the research data were processed throughout the workflow, including artifact identification, patch and binary-mask extraction, quality control, graph-based cleaning, patient-level partitioning into training, validation, and test sets, color-normalization strategies, smart sampling of training patches, learning-rate selection, supervised segmentation model training, ensemble optimization, and final inference/evaluation. The collection includes CSV manifests, SQLite provenance and execution databases, GeoJSON-related artifact metadata, patient-level split artifacts, sampling records, model-training metadata, ensemble configuration files, and quantitative evaluation outputs. Its purpose is to support verification, traceability, and reproducibility of the computational methodology used to study prostate cancer segmentation in histopathological images. (2026-08-09)
Subject Computer and Information Science; Engineering; Mathematical Sciences; Medicine, Health and Life Sciences
Keyword Cancer, Artificial Intelligence, Digital Pathology
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metadata.zip
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Published Sep 14, 2026
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MD5: e4a3da08892d9f2cbc53ea6bdd1d6cde
This repository contains the metadata, databases, intermediate artifacts, and experimental results required to understand and reproduce the computational workflow developed for the master’s dissertation “An Automated and Reproducible Method for Cancer Identification in Histopathological Slide Images". The source of the data is DIAGSET Prostate Cancer Dataset.
README.txt
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Published Sep 14, 2026
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MD5: 0eca8aefcadc83ddf461e60b57d7c8a8
Brief explanation of the Metadata Organizational structure.
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