[IMAVIS] Official implementation of "ASF-YOLO: A Novel YOLO Model with Attentional Scale Sequence Fusion for Cell Instance Segmentation".
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Updated
Jul 20, 2024 - Python
[IMAVIS] Official implementation of "ASF-YOLO: A Novel YOLO Model with Attentional Scale Sequence Fusion for Cell Instance Segmentation".
Automated 3D cell detection in very large images
Scalable Instance Segmentation using PyTorch & PyTorch Lightning.
SuperDSM is a globally optimal segmentation method based on superadditivity and deformable shape models for cell nuclei in fluorescence microscopy images and beyond.
MagellanMapper is a graphical interface for 3D bioimage annotation, atlas registration, and regional quantification
Approach that won 3rd place in the OCELOT 2023 Challenge. Multi-organ H&E-based deep learning model for cell detection, applicable for tumor cellularity/ purity/ content estimation.
Haar feature-based cascade classifier to detect infected cells with Malaria
Cell localization and counting: 1) Exponential Distance Transform Maps for Cell Localization; 2) Multi-scale Hypergraph-based Feature Alignment Network for Cell Localization; 3) Lite-UNet: A lightweight and efficient network for cell localization
Region-based Fitting of Overlapping Ellipses (original implementation by C. Panagiotakis and A.A. Argyros, Image Vis Comput 2020)
Efficient cell detection in large images using cellfinder in napari
Standalone cellfinder cell detection algorithm
Python code for merging and refinement of detected neurons in large 3D stacks
OCELOT 2023: Cell Detection from Cell-Tissue Interaction
Cell Detection and Cell Segmentation
SAM on medical images based on https://github.com/facebookresearch/segment-anything
Harness deep learning and bounding boxes to perform object detection, segmentation, tracking and more.
Visualisation and analysis of brain imaging data
BCCD (Blood Cell Count and Detection) Dataset is a small-scale dataset for blood cells detection.
nucleus/cell and histopathology image classification,detection,segmentation
Detecting and Tracking cancer (HeLa) cells using Computer Vision techniques. The project also detects cell division and analyses cell motion such as speed, distance travelled etc. The project uses OpenCV3 for image processing.
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