Engineered feature embeddings meet deep learning: A novel strategy to improve bone marrow cell classification and model transparency
Cytomorphology evaluation of bone marrow cell is the initial step to diagnose different hematological diseases. This assessment is still manually performed by trained specialists, who may be a bottleneck within the clinical process. Deep learning algorithms are a promising approach to automate this...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Journal of Pathology Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2153353924000294 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850257571481387008 |
|---|---|
| author | Jonathan Tarquino Jhonathan Rodríguez David Becerra Lucia Roa-Peña Eduardo Romero |
| author_facet | Jonathan Tarquino Jhonathan Rodríguez David Becerra Lucia Roa-Peña Eduardo Romero |
| author_sort | Jonathan Tarquino |
| collection | DOAJ |
| description | Cytomorphology evaluation of bone marrow cell is the initial step to diagnose different hematological diseases. This assessment is still manually performed by trained specialists, who may be a bottleneck within the clinical process. Deep learning algorithms are a promising approach to automate this bone marrow cell evaluation. These artificial intelligence models have focused on limited cell subtypes, mainly associated to a particular disease, and are frequently presented as black boxes. The herein introduced strategy presents an engineered feature representation, the region-attention embedding, which improves the deep learning classification performance of a cytomorphology with 21 bone marrow cell subtypes. This embedding is built upon a specific organization of cytology features within a squared matrix by distributing them after pre-segmented cell regions, i.e., cytoplasm, nucleus, and whole-cell. This novel cell image representation, aimed to preserve spatial/regional relations, is used as input of the network. Combination of region-attention embedding and deep learning networks (Xception and ResNet50) provides local relevance associated to image regions, adding up interpretable information to the prediction. Additionally, this approach is evaluated in a public database with the largest number of cell subtypes (21) by a thorough evaluation scheme with three iterations of a 3-fold cross-validation, performed in 80% of the images (n = 89,484), and a testing process in an unseen set of images composed by the remaining 20% of the images (n = 22,371). This evaluation process demonstrates the introduced strategy outperforms previously published approaches in an equivalent validation set, with a f1-score of 0.82, and presented competitive results on the unseen data partition with a f1-score of 0.56. |
| format | Article |
| id | doaj-art-38685f3df52a4fb2a64f035d77c7d936 |
| institution | OA Journals |
| issn | 2153-3539 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Pathology Informatics |
| spelling | doaj-art-38685f3df52a4fb2a64f035d77c7d9362025-08-20T01:56:23ZengElsevierJournal of Pathology Informatics2153-35392024-12-011510039010.1016/j.jpi.2024.100390Engineered feature embeddings meet deep learning: A novel strategy to improve bone marrow cell classification and model transparencyJonathan Tarquino0Jhonathan Rodríguez1David Becerra2Lucia Roa-Peña3Eduardo Romero4Computer Imaging and Medical Application Laboratory, Universidad Nacional de Colombia, Bogotá 111321, ColombiaComputer Imaging and Medical Application Laboratory, Universidad Nacional de Colombia, Bogotá 111321, ColombiaDepartment of Pathology, School of Medicine, Universidad Nacional de Colombia, Bogotá, ColombiaDepartment of Pathology, School of Medicine, Universidad Nacional de Colombia, Bogotá, ColombiaComputer Imaging and Medical Application Laboratory, Universidad Nacional de Colombia, Bogotá 111321, Colombia; Corresponding author:Cytomorphology evaluation of bone marrow cell is the initial step to diagnose different hematological diseases. This assessment is still manually performed by trained specialists, who may be a bottleneck within the clinical process. Deep learning algorithms are a promising approach to automate this bone marrow cell evaluation. These artificial intelligence models have focused on limited cell subtypes, mainly associated to a particular disease, and are frequently presented as black boxes. The herein introduced strategy presents an engineered feature representation, the region-attention embedding, which improves the deep learning classification performance of a cytomorphology with 21 bone marrow cell subtypes. This embedding is built upon a specific organization of cytology features within a squared matrix by distributing them after pre-segmented cell regions, i.e., cytoplasm, nucleus, and whole-cell. This novel cell image representation, aimed to preserve spatial/regional relations, is used as input of the network. Combination of region-attention embedding and deep learning networks (Xception and ResNet50) provides local relevance associated to image regions, adding up interpretable information to the prediction. Additionally, this approach is evaluated in a public database with the largest number of cell subtypes (21) by a thorough evaluation scheme with three iterations of a 3-fold cross-validation, performed in 80% of the images (n = 89,484), and a testing process in an unseen set of images composed by the remaining 20% of the images (n = 22,371). This evaluation process demonstrates the introduced strategy outperforms previously published approaches in an equivalent validation set, with a f1-score of 0.82, and presented competitive results on the unseen data partition with a f1-score of 0.56.http://www.sciencedirect.com/science/article/pii/S2153353924000294Deep learningBone marrow cell subtypesCytomorphologyInterpretabilityBiomedical image processing |
| spellingShingle | Jonathan Tarquino Jhonathan Rodríguez David Becerra Lucia Roa-Peña Eduardo Romero Engineered feature embeddings meet deep learning: A novel strategy to improve bone marrow cell classification and model transparency Journal of Pathology Informatics Deep learning Bone marrow cell subtypes Cytomorphology Interpretability Biomedical image processing |
| title | Engineered feature embeddings meet deep learning: A novel strategy to improve bone marrow cell classification and model transparency |
| title_full | Engineered feature embeddings meet deep learning: A novel strategy to improve bone marrow cell classification and model transparency |
| title_fullStr | Engineered feature embeddings meet deep learning: A novel strategy to improve bone marrow cell classification and model transparency |
| title_full_unstemmed | Engineered feature embeddings meet deep learning: A novel strategy to improve bone marrow cell classification and model transparency |
| title_short | Engineered feature embeddings meet deep learning: A novel strategy to improve bone marrow cell classification and model transparency |
| title_sort | engineered feature embeddings meet deep learning a novel strategy to improve bone marrow cell classification and model transparency |
| topic | Deep learning Bone marrow cell subtypes Cytomorphology Interpretability Biomedical image processing |
| url | http://www.sciencedirect.com/science/article/pii/S2153353924000294 |
| work_keys_str_mv | AT jonathantarquino engineeredfeatureembeddingsmeetdeeplearninganovelstrategytoimprovebonemarrowcellclassificationandmodeltransparency AT jhonathanrodriguez engineeredfeatureembeddingsmeetdeeplearninganovelstrategytoimprovebonemarrowcellclassificationandmodeltransparency AT davidbecerra engineeredfeatureembeddingsmeetdeeplearninganovelstrategytoimprovebonemarrowcellclassificationandmodeltransparency AT luciaroapena engineeredfeatureembeddingsmeetdeeplearninganovelstrategytoimprovebonemarrowcellclassificationandmodeltransparency AT eduardoromero engineeredfeatureembeddingsmeetdeeplearninganovelstrategytoimprovebonemarrowcellclassificationandmodeltransparency |