Z. Mousavi Kouzehkanan

This paper proposes a deep learning framework for automated segmentation of PDAC and surrounding vessels in CT images, achieving improved Dice scores using 3D-CNN and texture descriptors.

The paper introduces a method to improve WBC classification generalization by using a novel image representation and CNN training, achieving 93.97% accuracy on Raabin-WBC and 51.57% on LISC.

The paper introduces Raabin-WBC, a dataset of white blood cells for machine learning tasks, showing how data diversity impacts model generalization.

The paper introduces a new image processing and machine learning method for classifying white blood cells with high accuracy using three datasets.