The article discusses the implementation of modern classification models of artificial intelligence in the training of pathologists. The software package "GistoDiagnozUZ" has been developed, including modules for preprocessing, segmentation and classification of histological images. Experiments on open and proprietary datasets have shown an increase in the AUROC value to 0.93, a reduction in the time for slide analysis by almost half and a decrease in the cognitive load of pathologists. The results confirm that the integration of transfer learning and original CNN architectures improves diagnostic skills and can become the basis for standardized AI support in educational programs on pathomorphology.
| Mualliflar | Meliev, Farxod, Шадиев, У.Р., Мелиев, Ф.М., Маликов, З.А. |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2025-09-12 |
| Son | 3 |
| Betlar | 19-22 |
| Til | Rus |
классификационные модели, трансферное обучение, CNN-архитектура., classification models, transfer learning, CNN architecture., tasniflash modellari, transferni o'rganish, CNN arxitekturasi.
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