Csaba Molnár

The paper introduces a subphase-labeled mitotic dataset to improve mitosis detection and subtyping, enhancing domain generalization and enabling biological insights into mitotic subphases.

Piximi provides a no-programming web tool for biological image analysis using deep learning, enabling rapid insights without installation or coding.

The paper introduces Regression Plane, a tool for continuous phenotypic analysis using machine learning without requiring class labels.

The paper introduces a deep learning framework for nucleus segmentation that uses image style transfer to adapt to new data without expert annotations, outperforming existing methods on diverse microscopy images.

This study evaluates deep learning strategies for nucleus segmentation in fluorescence images, showing U-Net outperforms classical methods and reduces biologically relevant errors.

This paper develops a deep convolutional neural network for automated astrocyte detection in microscopy images, achieving accuracy comparable to human experts and faster runtime than existing methods.

The paper shows that incorporating microenvironmental features improves machine learning-based single-cell phenotyping in imaging data.