Takashi Morikura

This paper introduces a deep learning model to predict gene regulation relationships for both known and unknown transcription factors, achieving higher accuracy for unknown TFs than existing methods.

This paper introduces an unsupervised domain adaptation method for cell segmentation that avoids predefined thresholds, achieving accuracy comparable to supervised models without labeled data.

This paper introduces an autofluorescence-based cell counting method for suspension cultures using microcarriers, demonstrating accurate cell number estimation without parameter tuning or fluorescence labeling.

This paper develops a deep learning model to automatically predict mouse seminiferous tubule stages from bright-field microscopy images, achieving 98.33% accuracy with a ±1 stage tolerance.

This paper develops a new image-to-image translation model for label-free multiplex microscopic imaging, achieving high performance at both pixel and image levels.