Histopathology Imagery Dataset of Ph-Negative Myeloproliferative Neoplasm.v2

Published: 11 July 2023| Version 1 | DOI: 10.17632/hbdh66ws8d.1
Umi Kalsom M.Yusof, Syamsiah Mashohor, Marsyita Hanafi, Sabariah Md Noor, Norsafina Zainal


Despite advanced technology in diagnostic tools to boost the procedure, the morphological assessment of bone marrow trephine (BMT) images remains critical to confirm and differentiate MPN subtypes. Hence, this histopathological imagery dataset was generated and focuses on the most typical MPN from Philadelphia chromosome (Ph)-negative type, which are Essential Thrombocythemia (ET), Polycythemia Vera (PV), Primary Myelofibrosis (MF). This dataset consists of 300 BMT images which can be used to enable computer vision applications such as image segmentation, disease classification, and object recognition. Hence, the application can assist medical practitioners to overcome current challenges such as high dependency on human expertise and misdiagnosis.



Universiti Putra Malaysia


Artificial Intelligence, Machine Learning, Histopathology, Myeloproliferative Neoplasm