ACCV'18 workshop - Auto-Classification of Retinal Diseases in the Limit of Sparse Data Using a Two-Streams Machine Learning Model
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Updated
Jul 5, 2018 - Python
ACCV'18 workshop - Auto-Classification of Retinal Diseases in the Limit of Sparse Data Using a Two-Streams Machine Learning Model
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Code for regenerating the random-number-based contrast values used for white-noise stimulation in the Gollisch Lab. Used for both temporal and spatiotemporal stimuli.
Using OpenCV's Bioinspired Module Retina to enhance low(er) quality photos from e.g. an old compact camera.
A fast and versatile implementation of spike-triggered non-negative matrix factorization (STNMF) based on accelerated/fast HALS algorithms
A python wrapper for developing, organizing, and deploying vision science experiments.
A tensorflow implementation of "Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks"
Useful tools to compress and decompress AEDAT files in Python
a (simplistic) neuromimetic retina model based on open hardware such as the raspberry π
Hourglass network for topology extraction of retina images
This project serves as a prime example of computer vision's role in revolutionizing healthcare. By utilizing the Detectron2 framework this project enables accurate detection of tumors in brain MRI images. The resultant web application, developed using Streamlit, provides a user-friendly interface for visualizing these detections.
Collection of Python scripts for analyzing extracellular multielectrode array recordings from the retina in the Gollisch lab, Göttingen.
Data and example scripts used in the paper `Inferring hidden structure in multilayered neural circuits`
Python package to read Heidelberg Spectralis files
An implementation of《Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks》
Project for segmentation of blood vessels, microaneurysm and hardexudates in fundus images.
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