{"payload":{"pageCount":3,"repositories":[{"type":"Public","name":"retinal-rl","owner":"berenslab","isFork":false,"description":"Testing theories about retinal coding in reinforcement learning environments","allTopics":[],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":0,"forksCount":1,"license":"GNU Affero General Public License v3.0","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-09-13T07:38:29.515Z"}},{"type":"Public","name":"retimgtools","owner":"berenslab","isFork":false,"description":"Web-based tools for retinal image annotation and evaluation.","allTopics":[],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":1,"forksCount":0,"license":null,"participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-08-27T07:22:19.021Z"}},{"type":"Public","name":"RFEst","owner":"berenslab","isFork":false,"description":"A Python 3 toolbox for neural receptive field estimation using splines and Gaussian priors.","allTopics":["matrix-factorization","splines","jax","receptive-field","evidence-optimization"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":4,"starsCount":24,"forksCount":3,"license":"GNU General Public License v3.0","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-08-20T11:13:42.497Z"}},{"type":"Public","name":"medical-t-simcne","owner":"berenslab","isFork":false,"description":"This repository contains the codes to train a t-SimCNE model. This model has been shown to produce good representations on natural and medical images.","allTopics":["visualisation","medical-imaging","representation-learning"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":2,"forksCount":1,"license":null,"participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-08-12T11:16:15.849Z"}},{"type":"Public","name":"hh_sbi","owner":"berenslab","isFork":false,"description":"","allTopics":[],"primaryLanguage":{"name":"Jupyter Notebook","color":"#DA5B0B"},"pullRequestCount":0,"issueCount":0,"starsCount":1,"forksCount":0,"license":"GNU General Public License v3.0","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-08-05T15:11:21.220Z"}},{"type":"Public","name":"elephant-in-the-room","owner":"berenslab","isFork":false,"description":"Companion repository to our Lause, Berens & Kobak (2024) preprint \"The art of seeing the elephant in the room: 2D embeddings of single-cell data do make sense\" (bioRxiv)","allTopics":[],"primaryLanguage":{"name":"Jupyter Notebook","color":"#DA5B0B"},"pullRequestCount":0,"issueCount":0,"starsCount":5,"forksCount":0,"license":"GNU General Public License v3.0","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-07-31T07:44:16.905Z"}},{"type":"Public","name":"dependence-measures-medical-imaging","owner":"berenslab","isFork":false,"description":"This repository contains the code for the paper \"Benchmarking Dependence Measures to Prevent Shortcut Learning in Medical Imaging\".","allTopics":[],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":0,"forksCount":0,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-07-26T15:16:37.479Z"}},{"type":"Public","name":"read-normalization","owner":"berenslab","isFork":false,"description":"Companion repository to our Lause et al. (2023) preprint \"Compound models and Pearson residuals for normalization of single-cell RNA-seq data without UMIs\" (bioRxiv))","allTopics":[],"primaryLanguage":{"name":"Jupyter Notebook","color":"#DA5B0B"},"pullRequestCount":0,"issueCount":0,"starsCount":3,"forksCount":0,"license":"GNU General Public License v3.0","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-07-26T07:04:33.416Z"}},{"type":"Public","name":"MorphoPy","owner":"berenslab","isFork":false,"description":"","allTopics":[],"primaryLanguage":{"name":"Jupyter Notebook","color":"#DA5B0B"},"pullRequestCount":4,"issueCount":1,"starsCount":34,"forksCount":11,"license":"GNU General Public License v3.0","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-07-24T14:32:42.210Z"}},{"type":"Public","name":"dLGN_movie_spline_model","owner":"berenslab","isFork":false,"description":"dLGN movie spline LNP model","allTopics":[],"primaryLanguage":{"name":"Jupyter Notebook","color":"#DA5B0B"},"pullRequestCount":0,"issueCount":0,"starsCount":0,"forksCount":0,"license":"Creative Commons Zero v1.0 Universal","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-07-22T10:04:28.121Z"}},{"type":"Public","name":"fundus_image_toolbox","owner":"berenslab","isFork":false,"description":"A Python package for fundus image processing.","allTopics":[],"primaryLanguage":{"name":"Jupyter Notebook","color":"#DA5B0B"},"pullRequestCount":0,"issueCount":0,"starsCount":2,"forksCount":1,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-07-17T12:55:52.280Z"}},{"type":"Public","name":"interpretable-deep-survival-analysis","owner":"berenslab","isFork":false,"description":"An interpretable end-to-end CNN for disease progression modeling that predicts late AMD onset (MICCAI 2024)","allTopics":["deep-learning","cnn","survival-analysis","interpretability","disease-prediction","image-based","coxph"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":0,"forksCount":0,"license":null,"participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-07-17T12:51:49.579Z"}},{"type":"Public","name":"chatgpt-excess-words","owner":"berenslab","isFork":false,"description":"Delving into ChatGPT usage in academic writing through excess vocabulary","allTopics":[],"primaryLanguage":{"name":"Jupyter Notebook","color":"#DA5B0B"},"pullRequestCount":0,"issueCount":0,"starsCount":28,"forksCount":4,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-07-03T09:56:27.333Z"}},{"type":"Public","name":"ne_spectrum_scRNAseq","owner":"berenslab","isFork":false,"description":"","allTopics":[],"primaryLanguage":{"name":"Jupyter Notebook","color":"#DA5B0B"},"pullRequestCount":0,"issueCount":0,"starsCount":2,"forksCount":1,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-06-27T15:10:34.431Z"}},{"type":"Public","name":"DiffusionTempering","owner":"berenslab","isFork":false,"description":"","allTopics":[],"primaryLanguage":{"name":"Jupyter Notebook","color":"#DA5B0B"},"pullRequestCount":0,"issueCount":0,"starsCount":1,"forksCount":0,"license":null,"participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-29T10:55:03.311Z"}},{"type":"Public","name":"retinal-rl-models","owner":"berenslab","isFork":false,"description":"Code for the pytorch models used in the retinal-rl project (and related non-rl usages)","allTopics":[],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":5,"starsCount":0,"forksCount":0,"license":null,"participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-29T08:34:22.593Z"}},{"type":"Public","name":"retinal-classification","owner":"berenslab","isFork":false,"description":"non-rl experiments for the retinal-rl project","allTopics":[],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":0,"forksCount":0,"license":"GNU Affero General Public License v3.0","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-21T05:55:10.121Z"}},{"type":"Public","name":"Retinal-Vessel-Segmentation-Benchmark","owner":"berenslab","isFork":false,"description":"","allTopics":[],"primaryLanguage":{"name":"Jupyter Notebook","color":"#DA5B0B"},"pullRequestCount":0,"issueCount":0,"starsCount":0,"forksCount":1,"license":null,"participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-14T11:23:08.689Z"}},{"type":"Public","name":"eff-ph","owner":"berenslab","isFork":false,"description":"Persistent homology for high-dimensional data based on spectral methods","allTopics":[],"primaryLanguage":{"name":"Jupyter Notebook","color":"#DA5B0B"},"pullRequestCount":0,"issueCount":0,"starsCount":2,"forksCount":1,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-08T11:52:37.507Z"}},{"type":"Public","name":"neuroprobnum","owner":"berenslab","isFork":false,"description":"Probabilistic numerics for common neuroscience models.","allTopics":[],"primaryLanguage":{"name":"Jupyter Notebook","color":"#DA5B0B"},"pullRequestCount":0,"issueCount":0,"starsCount":5,"forksCount":1,"license":"GNU General Public License v3.0","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-07T12:47:09.683Z"}},{"type":"Public","name":"iclr-dataset","owner":"berenslab","isFork":false,"description":"ICLR dataset","allTopics":[],"primaryLanguage":{"name":"Jupyter Notebook","color":"#DA5B0B"},"pullRequestCount":0,"issueCount":1,"starsCount":8,"forksCount":0,"license":null,"participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-02T11:32:57.978Z"}},{"type":"Public","name":"contrastive-ne","owner":"berenslab","isFork":false,"description":"Contrastive neighbor embeddings","allTopics":[],"primaryLanguage":{"name":"Jupyter Notebook","color":"#DA5B0B"},"pullRequestCount":0,"issueCount":1,"starsCount":51,"forksCount":6,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-04-24T09:38:00.589Z"}},{"type":"Public","name":"pubmed-landscape","owner":"berenslab","isFork":false,"description":"The landscape of biomedical research","allTopics":[],"primaryLanguage":{"name":"Jupyter Notebook","color":"#DA5B0B"},"pullRequestCount":0,"issueCount":0,"starsCount":113,"forksCount":9,"license":null,"participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-04-18T12:53:32.509Z"}},{"type":"Public","name":"poisson-gpfa","owner":"berenslab","isFork":true,"description":"Gaussian process factor analysis with Poisson observations","allTopics":[],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":0,"forksCount":15,"license":"BSD 2-Clause \"Simplified\" License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-04-11T11:39:11.361Z"}},{"type":"Public","name":"t-simcne","owner":"berenslab","isFork":false,"description":"Unsupervised visualization of image datasets using contrastive learning","allTopics":["visualization","dimensionality-reduction"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":1,"issueCount":2,"starsCount":115,"forksCount":13,"license":null,"participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-03-27T13:23:01.917Z"}},{"type":"Public","name":"picasso","owner":"berenslab","isFork":true,"description":"Picasso: a methods for embedding points in 2D in a way that respects distances while fitting a user-specified shape.","allTopics":[],"primaryLanguage":{"name":"Jupyter Notebook","color":"#DA5B0B"},"pullRequestCount":0,"issueCount":0,"starsCount":0,"forksCount":7,"license":"BSD 2-Clause \"Simplified\" License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-02-22T14:01:02.622Z"}},{"type":"Public","name":"MIDL24-segmentation_quality_control","owner":"berenslab","isFork":false,"description":"Experiments for MIDL 2024 Submission: Controlling Segmentation Quality per Image","allTopics":[],"primaryLanguage":{"name":"Jupyter Notebook","color":"#DA5B0B"},"pullRequestCount":0,"issueCount":0,"starsCount":1,"forksCount":0,"license":null,"participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-02-11T21:31:06.519Z"}},{"type":"Public","name":"fundus_circle_cropping","owner":"berenslab","isFork":false,"description":"Code for extracting a circular mask from fundus images and tightly cropping the images around the mask.","allTopics":[],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":2,"forksCount":1,"license":"Other","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-02-07T13:32:01.234Z"}},{"type":"Public","name":"retinal_image_counterfactuals","owner":"berenslab","isFork":false,"description":"Realistic retinal fundus and OCT counterfactuals using diffusion models and classifiers. ","allTopics":[],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":1,"starsCount":3,"forksCount":0,"license":null,"participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2023-12-05T15:00:34.700Z"}},{"type":"Public","name":"MA_LA_For_MML","owner":"berenslab","isFork":true,"description":"Code for my Masters Thesis \"Robust Uncertainty Estimation in Medical Machine Learning Applications with the Laplace Approximation\". Mostly based on Laplace Redux -- Effortless Bayesian Deep Learning","allTopics":[],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":0,"forksCount":7,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2023-11-16T19:51:18.381Z"}}],"repositoryCount":70,"userInfo":null,"searchable":true,"definitions":[],"typeFilters":[{"id":"all","text":"All"},{"id":"public","text":"Public"},{"id":"source","text":"Sources"},{"id":"fork","text":"Forks"},{"id":"archived","text":"Archived"},{"id":"template","text":"Templates"}],"compactMode":false},"title":"berenslab repositories"}