Automatic tardigrade biomass estimation in microscopic images.
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Updated
Jun 30, 2024 - Jupyter Notebook
Automatic tardigrade biomass estimation in microscopic images.
The Art and Science of Photography & Microscopic Photography Prints
End to end cell tracking code for TUBITAK 119e578 project.
Deep Learning-Based Object Detection and Bacteria Morphological Feature Extraction for Antimicrobial Resistance Applications
Microstructure vision-based porosity analysis
CoND: Classification of Neuronal Differentiation
A Fast Algorithm for Material Image Sequential Stitching
Convolutional Neural Network-Based Instance Segmentation Algorithm to Acquire Quantitative Criteria of Early Mouse Development
Recovering Microscopic Images in Material Science Documents by Image Inpainting
A mini dataset of lithology microscopic images. This Dataset was developed under supervision of Dr. Keyvan RahimiZadeh and in collabotion with Prof. Amin Beheshti.
Evaluating adipocyte differentiation of bone marrow-derived mesenchymal stem cells by a deep learning method for automatic lipid droplet counting
🧪 Reproducing the concept of Confocal Laser Scanning Microscope. Using Arduino and easily found materials. Generating images in Grayscale just for fun.
A generalized deep learning-based framework for assistance to the human malaria diagnosis from microscopic images
Panoramic image generation from 2D microscope images
Procesamiento de Imágenes Microsópicas
Malaria is a life-threatening disease that is spread by the Plasmodium parasites. It is detected by trained microscopists who analyze microscopic blood smear images. Modern deep learning techniques may be used to do this analysis automatically. The need for the trained personnel can be greatly reduced with the development of an automatic accurat…
Cancer Detection from Microscopic Images by Fine-tuning Pre-trained Models ("Inception") for new class labels
"Deep Learning based Automatic Inpainting for Material Microscopic Images" implemented by PyTorch
IDC prediction in breast cancer histopathology images using deep residual learning with an accuracy of 99.37% in a subset of images containing a total of 7,500 microscopic images.
Convolutional Neural Network-Based Algorithm to Predict the Future Direction of Cell Movement
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