Book club topics of this week were:.- Growing Neural Cellular Automata: - HyperNCA: - Medical: Retinoblastoma (cancer in the eye, almost exclusively found in young children).

Another deep learning for ophthalmology story is online in the December version of the @RSIPvision News! 👀👀 This time, I interviewed @ignaciorlando on how to go the extra mile for creating a product out of research! You can read the full story here:.

RT @INSIGHTeyehub: 📢👁️Applications are open for a funded PhD Studentship in Artificial Intelligence for Ocular Imaging @UCLeye @UCLBiochemE….

Also, the preprint is available on arxiv: and you can find the MICCAI 2023 version here:

For the November edition of the @RSIPvision computer vision magazine, I interviewed Robbie Holland from @BioMedIAICL about his work on temporal biomarker detection for age-related macular degeneration in OCT scans:

Also, to make it possible for other researchers in the field to work with their pre-trained foundation model, they made the code as well as weight files available on GitHub:

Their paper recently got published in nature! You can find the full publication here:

Another month, another datascEYEnce highlight! For the October version of the @RSIPvision Computer Vision Magazine, I interviewed @Yukunzhou19 about RETFound - a foundation model for retinal images:

RT @mofuchs1: Huge thanks to @RSIPvision and @variint for featuring our synthetic cataract surgery research! . Thanks to @softwarecampus….

RT @YannikFrisch: Huge thanks to @RSIPvision and @variint for featuring our research paper on synthetic cataract surgery 💻👁️.Honored to see….

His publication "Synthesising Rare Cataract Surgery Samples with Guided Diffusion Models" will soon appear at #MICCAI2023! Until then, you can find the arXiv version here: and you can find their code on GitHub:

Hello everyone! This month, I am introducing you to @YannikFrisch and his work on diffusion models applied to cataract surgery videos! Read the whole story here:

News from the deep learning for ophthalmology field - RETFound just got published in Nature! In a self-supervised phase, a good representation of the eye is learned and can then be fine-tuned with a smaller labelled dataset for disease detection!.

Also, in case you are attending #MICCAI2023, watch out for his newest research project "Self-supervised learning via inter-modal reconstruction and feature projection networks for label-efficient 3D-to-2D segmentation" -

On page 27 of the Computer Vision News, you can find some more implementation details!

You can find the original publication "Weakly-supervised detection of AMD-related lesions in color fundus images using explainable deep learning" here:

Hello #ophthotwitter! You can read the first interview of the datascEYEnce column in Computer Vision News by @RSIPvision now!! The topic is a recent publication by José Morano Sánchez on multiple instance learning applied to AMD lesions in fundus images:

Deep learning session intro in the lab!! We covered PyTorch datasets, samplers and dataloaders!

More detailed information can be found in the paper: and their code is available here:

Due to the large impact on the topology when only a few pixels are misclassified, they introduce a penalty term that considers the number of connected components and number of holes. Additionally, they enhance their method through the introduction of "recursive refinement."