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A Deep Learning Model for Automated Segmentation of Fluorescence Cell images
(IOP Publishing Ltd, 2021)
Deep learning techniques bring together key advantages in biomedical image
segmentation. They speed up the process, increase the reproducibility, and reduce the workload
in segmentation and classification. Deep learning ...
Az Örnekli Öğrenme ile Mikroskobik Görüntülerden Maya Hücresi Segmentasyonu
(IEEE, 2021)
Mikroskobik görüntülerden otomatik hücre segmentasyonu,
derin sinir ağları veya görüntü işleme teknikleri kullanılarak
yapılabilmektedir. Bu tekniklerin ayrı ayrı problemleri
ve zorlukları bulunmakla birlikte özellikle ...
DeepCAN: A Modular Deep Learning System for Automated Cell Counting and Viability Analysis
(IEEE, 2022)
Precise and quick monitoring of key cytometric features such as cell count, cell size, cell morphology,
and DNA content is crucial for applications in biotechnology, medical sciences, and cell culture research. Traditionally, ...
Enhancing Resolution and Contrast in Fibre Bundle-Based Fluorescence Microscopy Using Generative Adversarial Network
(Wiley, 2024)
Fibre bundle (FB)-based endoscopes are indispensable in biology and medical
science due to their minimally invasive nature. However, resolution and contrast
for fluorescence imaging are limited due to characteristic ...