Histopathology imagery dataset of Ph-negative myeloproliferative neoplasm
Tumorous cancer has been a widely known and well-studied medical phenomenon; however, rare diseases like Myeloproliferative Neoplasm (MPN) have received less attention, leading to delayed diagnosis. Despite the availability of advanced technology in diagnostic tools that can boost the procedure, the...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Published: |
Elsevier BV
2023
|
| Online Access: | http://psasir.upm.edu.my/id/eprint/108186/ |
| _version_ | 1848865096029175808 |
|---|---|
| author | Mohamad Yusof, Umi Kalsom Mashohor, Syamsiah Hanafi, Marsyita Md Noor, Sabariah Zainal, Norsafina |
| author_facet | Mohamad Yusof, Umi Kalsom Mashohor, Syamsiah Hanafi, Marsyita Md Noor, Sabariah Zainal, Norsafina |
| author_sort | Mohamad Yusof, Umi Kalsom |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Tumorous cancer has been a widely known and well-studied medical phenomenon; however, rare diseases like Myeloproliferative Neoplasm (MPN) have received less attention, leading to delayed diagnosis. Despite the availability of advanced technology in diagnostic tools that can boost the procedure, the morphological assessment of bone marrow trephine (BMT) images remains critical to confirm and differentiate MPN subtypes. This paper reports a histopathological imagery dataset that was created to focus on the most common MPN from the Philadelphia Chromosome (Ph)-negative type, namely Essential Thrombocythemia (ET), Polycythemia Vera (PV), and Primary Myelofibrosis (MF). The dataset consisted of 300 BMT images that can be used to enable computer vision applications, such as image segmentation, disease classification, and object recognition, in assisting the classification of the MPN disease. Ethical approval was obtained from the Ministry of Health, Malaysia and the bone marrow trephine images were captured using a digital microscope from the Olympus model (BX41 Dual head microscope) with x10, x20, and x40 lens types. The development of comprehensive tools deployed from this dataset can assist medical practitioners in diagnosing diseases, thus overcoming the current challenges. |
| first_indexed | 2025-11-15T13:59:16Z |
| format | Article |
| id | upm-108186 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:59:16Z |
| publishDate | 2023 |
| publisher | Elsevier BV |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1081862024-09-23T02:21:21Z http://psasir.upm.edu.my/id/eprint/108186/ Histopathology imagery dataset of Ph-negative myeloproliferative neoplasm Mohamad Yusof, Umi Kalsom Mashohor, Syamsiah Hanafi, Marsyita Md Noor, Sabariah Zainal, Norsafina Tumorous cancer has been a widely known and well-studied medical phenomenon; however, rare diseases like Myeloproliferative Neoplasm (MPN) have received less attention, leading to delayed diagnosis. Despite the availability of advanced technology in diagnostic tools that can boost the procedure, the morphological assessment of bone marrow trephine (BMT) images remains critical to confirm and differentiate MPN subtypes. This paper reports a histopathological imagery dataset that was created to focus on the most common MPN from the Philadelphia Chromosome (Ph)-negative type, namely Essential Thrombocythemia (ET), Polycythemia Vera (PV), and Primary Myelofibrosis (MF). The dataset consisted of 300 BMT images that can be used to enable computer vision applications, such as image segmentation, disease classification, and object recognition, in assisting the classification of the MPN disease. Ethical approval was obtained from the Ministry of Health, Malaysia and the bone marrow trephine images were captured using a digital microscope from the Olympus model (BX41 Dual head microscope) with x10, x20, and x40 lens types. The development of comprehensive tools deployed from this dataset can assist medical practitioners in diagnosing diseases, thus overcoming the current challenges. Elsevier BV 2023 Article PeerReviewed Mohamad Yusof, Umi Kalsom and Mashohor, Syamsiah and Hanafi, Marsyita and Md Noor, Sabariah and Zainal, Norsafina (2023) Histopathology imagery dataset of Ph-negative myeloproliferative neoplasm. Data in Brief, 50. art. no. 109484. pp. 1-6. ISSN 2352-3409 https://linkinghub.elsevier.com/retrieve/pii/S235234092300584X 10.1016/j.dib.2023.109484 |
| spellingShingle | Mohamad Yusof, Umi Kalsom Mashohor, Syamsiah Hanafi, Marsyita Md Noor, Sabariah Zainal, Norsafina Histopathology imagery dataset of Ph-negative myeloproliferative neoplasm |
| title | Histopathology imagery dataset of Ph-negative myeloproliferative neoplasm |
| title_full | Histopathology imagery dataset of Ph-negative myeloproliferative neoplasm |
| title_fullStr | Histopathology imagery dataset of Ph-negative myeloproliferative neoplasm |
| title_full_unstemmed | Histopathology imagery dataset of Ph-negative myeloproliferative neoplasm |
| title_short | Histopathology imagery dataset of Ph-negative myeloproliferative neoplasm |
| title_sort | histopathology imagery dataset of ph-negative myeloproliferative neoplasm |
| url | http://psasir.upm.edu.my/id/eprint/108186/ http://psasir.upm.edu.my/id/eprint/108186/ http://psasir.upm.edu.my/id/eprint/108186/ |