biomedical-image-processing
There are 170 repositories under biomedical-image-processing topic.
Dana-Farber-AIOS/pathml
Tools for computational pathology
seung-lab/connected-components-3d
Connected components on discrete and continuous multilabel 3D & 2D images. Handles 26, 18, and 6 connected variants; periodic boundaries (4, 8, & 6)
cambridgeltl/visual-med-alpaca
Visual Med-Alpaca is an open-source, multi-modal foundation model designed specifically for the biomedical domain, built on the LLaMa-7B.
jvanvugt/pytorch-unet
Tunable U-Net implementation in PyTorch
seung-lab/euclidean-distance-transform-3d
Euclidean distance & signed distance transform for multi-label 3D anisotropic images using marching parabolas.
OpenEIT/OpenEIT
Python based dashboard for real-time Electrical Impedance Tomography including image reconstruction using Back Projection, Graz Consensus and Gauss Newton methods
tkuanlun350/3DUnet-Tensorflow-Brats18
3D Unet biomedical segmentation model powered by tensorpack with fast io speed
mlcommons/GaNDLF
A generalizable application framework for segmentation, regression, and classification using PyTorch
zudi-lin/pytorch_connectomics
PyTorch Connectomics: segmentation toolbox for EM connectomics
seung-lab/kimimaro
Skeletonize densely labeled 3D image segmentations with TEASAR. (Medial Axis Transform)
ELEKTRONN/elektronn3
A PyTorch-based library for working with 3D and 2D convolutional neural networks, with focus on semantic segmentation of volumetric biomedical image data
anjanatiha/Pneumonia-Detection-from-Chest-X-Ray-Images-with-Deep-Learning
Detecting Pneumonia in Chest X-ray Images using Convolutional Neural Network and Pretrained Models
seung-lab/cloud-volume
Read and write Neuroglancer datasets programmatically.
sacmehta/YNet
Y-Net: Joint Segmentation and Classification for Diagnosis of Breast Biopsy Images
BiaPyX/BiaPy
Open source Python library for building bioimage analysis pipelines
yuanqing811/ISIC2018
ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection
mattmacy/torchbiomed
Datasets, Transforms and Utilities specific to Biomedical Imaging
mirzaevinom/promise12_segmentation
Codes that I have written to complete promise12 prostate segmentation competition.
amrzhd/MRISkullStripping
Developing a UNet3D model for accurate MRI skull stripping using the Calgary Campinas 359 dataset, enhancing neuroimaging preprocessing workflows.
seung-lab/dijkstra3d
Dijkstra's Shortest Path for 6, 18, and 26-Connected 3D (Volumetric) Image Volumes
google-research/sofima
Scalable Optical Flow-based Image Montaging and Alignment
beasygo1ng/OCT-Retinal-Layer-Segmenter
UNet based model that segment retina to 8 layers in OCT images
hoang-ho/Skin_Lesions_Classification_DCNNs
Transfer Learning with DCNNs (DenseNet, Inception V3, Inception-ResNet V2, VGG16) for skin lesions classification
seung-lab/fastremap
Remap, mask, renumber, unique, and in-place transposition of 3D labeled images. Point cloud too.
seung-lab/zmesh
Marching Cubes & Mesh Simplification on multi-label 3D images.
aangelopoulos/im2im-uq
Image-to-image regression with uncertainty quantification in PyTorch. Take any dataset and train a model to regress images to images with rigorous, distribution-free uncertainty quantification.
JiaxinZhuang/Skin-Lesion-Recognition.Pytorch
Rank3 Code for ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection, Task 3
RuiyangJu/Bone_Fracture_Detection_YOLOv8
Scientific Reports 2023
fangq/mmc
Mesh-based Monte Carlo (MMC)
vaibhavshukla182/Brain-MRI-Segmentation
Smart India Hackathon 2019 project given by the Department of Atomic Energy
jvwilliams23/pyssam
A Python library for biomedical statistical shape and appearance modelling.
anjanatiha/Cancer-Detection-from-Microscopic-Tissue-Images-with-Deep-Learning
Cancer Detection from Microscopic Images by Fine-tuning Pre-trained Models ("Inception") for new class labels
PathologyDataScience/HiPS
Histomic Prognostic Signature (HiPS): A population-level computational histologic signature for invasive breast cancer prognosis
sauravmishra1710/U-Net---Biomedical-Image-Segmentation
Implementation of the paper titled - U-Net: Convolutional Networks for Biomedical Image Segmentation @ https://arxiv.org/abs/1505.04597
sacmehta/WSISegmentation
Segmenting WSIs using Deep Convolutional Neural Networks
cviaai/BIA-course
A course in biomedical image analytics by Prof. Dmitry V. Dylov