Deep Clustering for Blood Cell Classification and Quantification
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911172294344704 |
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| author | Macarie-Ancau, Mihaela Groza, Adrian |
| author_facet | Macarie-Ancau, Mihaela Groza, Adrian |
| contents | Accurate classification of blood cells plays a key role in improving automated blood analysis for both medical and veterinary applications. This work presents a two-stage deep clustering method for classifying blood cells from high-dimensional signal data. In the first stage, red blood cells (RBCs) and platelets (PLTs) are separated using a combination of an improved autoencoder and the IDEC algorithm. The second stage further classifies RBC subtypes, pure RBCs, reticulocytes, and clumped RBCs, through a variational deep embedding (VaDE) approach. Due to the lack of detailed cell-level labels, soft classification probabilities are generated from sample-level data to approximate the true distributions. The aim is to contribute to the development of low-cost, automated blood analysis systems suitable for veterinary and biomedical use. Initial results indicate this method shows promise in effectively distinguishing different blood cell populations, even with limited supervision. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19399 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Clustering for Blood Cell Classification and Quantification Macarie-Ancau, Mihaela Groza, Adrian Quantitative Methods Accurate classification of blood cells plays a key role in improving automated blood analysis for both medical and veterinary applications. This work presents a two-stage deep clustering method for classifying blood cells from high-dimensional signal data. In the first stage, red blood cells (RBCs) and platelets (PLTs) are separated using a combination of an improved autoencoder and the IDEC algorithm. The second stage further classifies RBC subtypes, pure RBCs, reticulocytes, and clumped RBCs, through a variational deep embedding (VaDE) approach. Due to the lack of detailed cell-level labels, soft classification probabilities are generated from sample-level data to approximate the true distributions. The aim is to contribute to the development of low-cost, automated blood analysis systems suitable for veterinary and biomedical use. Initial results indicate this method shows promise in effectively distinguishing different blood cell populations, even with limited supervision. |
| title | Deep Clustering for Blood Cell Classification and Quantification |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2509.19399 |