microscopic-images
There are 18 repositories under microscopic-images topic.
Keep-Passion/ImageStitch
A Fast Algorithm for Material Image Sequential Stitching
funalab/QCANet
Convolutional Neural Network-Based Instance Segmentation Algorithm to Acquire Quantitative Criteria of Early Mouse Development
anjanatiha/Cancer-Detection-from-Microscopic-Tissue-Images-with-Deep-Learning
Cancer Detection from Microscopic Images by Fine-tuning Pre-trained Models ("Inception") for new class labels
fbasatemur/Microscopy_Image_Stitching
Panoramic image generation from 2D microscope images
biodatlab/bacteria-detection
Deep Learning-Based Object Detection and Bacteria Morphological Feature Extraction for Antimicrobial Resistance Applications
funalab/PredictMovingDirection
Convolutional Neural Network-Based Algorithm to Predict the Future Direction of Cell Movement
Benhabiles-projects/MalariaNet
A generalized deep learning-based framework for assistance to the human malaria diagnosis from microscopic images
melchisedech333/laser-scanning-microscopy
🧪 Reproducing the concept of Confocal Laser Scanning Microscope. Using Arduino and easily found materials. Generating images in Grayscale just for fun.
Chandra-cc/IDCBreastCancer_histopathologyImages_deepResidualLearning
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.
ustb-ai3d/automatic_inpainting
"Deep Learning based Automatic Inpainting for Material Microscopic Images" implemented by PyTorch
AhmadTaheri2021/Lithology-microscopic-images-mini-dataset
A mini dataset of lithology microscopic images. This Dataset was developed under supervision of Dr. Keyvan RahimiZadeh and in collabotion with Prof. Amin Beheshti.
apetsiuk/microstructure-vision-based-porosity-analysis
Microstructure vision-based porosity analysis
hmnd1257/threshold-mask
Recovering Microscopic Images in Material Science Documents by Image Inpainting
sarkerrabi/Malaria-detection-with-ML-kit
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 accurate and efficient model. In this article, we propose an entirely automated Convolutional Neural Network (CNN) based model for the diagnosis of malaria from the microscopic blood smear images. A variety of techniques including knowledge distillation, data augmentation, Autoencoder, feature extraction by a CNN model and classified by Support Vector Machine (SVM) or K-Nearest Neighbors (KNN) are performed under three training procedures named general training, distillation training and autoencoder training to optimize and improve the model accuracy and inference performance. Our deep learning-based model can detect malarial parasites from microscopic images with an accuracy of 99.23% while requiring just over 4600 floating point operations. For practical validation of model efficiency, we have deployed the miniaturized model in different mobile phones and a server-backed web application. Data gathered from these environments show that the model can be used to perform inference under 1 s per sample in both offline (mobile only) and online (web application) mode, thus engendering confidence that such models may be deployed for efficient practical inferential systems.
TheGrycek/Tardigrada
Automatic tardigrade biomass estimation in microscopic images.
funalab/CoND
CoND: Classification of Neuronal Differentiation
hassanlougithub/lipid_droplets_counting
Evaluating adipocyte differentiation of bone marrow-derived mesenchymal stem cells by a deep learning method for automatic lipid droplet counting
cabustillo13/Imagenes-microscopicas
Procesamiento de Imágenes Microsópicas