A Fast Algorithm for Material Image Sequential Stitching

Convolutional Neural Network-Based Instance Segmentation Algorithm to Acquire Quantitative Criteria of Early Mouse Development

Cancer Detection from Microscopic Images by Fine-tuning Pre-trained Models ("Inception") for new class labels

Panoramic image generation from 2D microscope images

Deep Learning-Based Object Detection and Bacteria Morphological Feature Extraction for Antimicrobial Resistance Applications

🧪 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

Convolutional Neural Network-Based Algorithm to Predict the Future Direction of Cell Movement

"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.

A mini dataset of lithology microscopic images. This Dataset was developed under supervision of Dr. Keyvan RahimiZadeh and in collabotion with Prof. Amin Beheshti.

Microstructure vision-based porosity analysis

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…

Microscopic images of Kitty's claws taken using the Quanta 600 FEG Environmental Scanning Electron Microscope in the Nanoscale Characterization Facility at the Krishna P. Singh Center for Nanotechnology.

Automatic tardigrade biomass estimation in microscopic images.

Microscopic images taken of milk (deciduous/baby) teeth from Juno the puppy gathered in Northern Philadelphia, PA, USA using the Quanta 600 FEG Environmental Scanning Electron Microscope in the Nanoscale Characterization Facility at the Krishna P. Singh Center for Nanotechnology.

Microscopic images taken using the Quanta 600 FEG Environmental Scanning Electron Microscope in the Nanoscale Characterization Facility at the Krishna P. Singh Center for Nanotechnology.

Microscopic images of Sylvester the fly gathered in northern Philadelphia, PA, USA using the Quanta 600 FEG Environmental Scanning Electron Microscope in the Nanoscale Characterization Facility at the Krishna P. Singh Center for Nanotechnology.

The Mouse Hippocampal Brain Stack Dataset is a high-resolution dataset of serial-section microscopic images of the hippocampal region in a mouse brain. It is designed to support research on 3D reconstruction and non-rigid registration techniques.

Evaluating adipocyte differentiation of bone marrow-derived mesenchymal stem cells by a deep learning method for automatic lipid droplet counting