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We proposed a novel U-Net-based model -- DC-UNet to do medical image segmentation.
Open-source software for exploring and analyzing large, high-dimensional image-derived data.
BIA Bob is a Jupyter+LLM-based assistant for interacting with image data and for working on Bio-image Analysis tasks.
Pure Python reader for MSR/OBF (Imspector) image data
Context Axial Reverse Attention Network for Small Medical Objects Segmentation
Implementation of the paper titled - U-Net: Convolutional Networks for Biomedical Image Segmentation @ https://arxiv.org/abs/1505.04597
TMI 2018. H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes
Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)
A Variational Autoencoder (VAE) implemented in PyTorch
The open source code of SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation.
Multimodal Brain mpMRI segmentation on BraTS 2023 and BraTS 2021 datasets.
A collection of source code for computing in the fields of mathematics, geometry, graphics, image analysis and physics.
Accelerated Pixel and Object Classifiers (APOC)
Deep-learning based semantic and instance segmentation for 3D Electron Microscopy and other bioimage analysis problems based on pytorch.
Software for writing protocols and running them on the Opentrons Flex and Opentrons OT-2
AUTOMATED TYPE CLASSIFICATION OF GLAUCOMA DETECTION USING DEEP LEARNING
Leica Image Format (LIF) file reader for Python
[unmaintained] An open-source convolutional neural networks platform for research in medical image analysis and image-guided therapy
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
Segment Anything for Microscopy
A CNN Variational Autoencoder (CNN-VAE) implemented in PyTorch