biomedical-image-processing

Tunable U-Net implementation in PyTorch

Tools for computational pathology

Connected components on discrete and continuous multilabel 3D & 2D images. Handles 26, 18, and 6 connected variants.

3D Unet biomedical segmentation model powered by tensorpack with fast io speed

Python based dashboard for real-time Electrical Impedance Tomography including image reconstruction using Back Projection, Graz Consensus and Gauss Newton methods

Euclidean distance & signed distance transform for multi-label 3D anisotropic images using marching parabolas.

A PyTorch-based library for working with 3D and 2D convolutional neural networks, with focus on semantic segmentation of volumetric biomedical image data

PyTorch Connectomics: segmentation toolbox for EM connectomics

Y-Net: Joint Segmentation and Classification for Diagnosis of Breast Biopsy Images

ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection

Detecting Pneumonia in Chest X-ray Images using Convolutional Neural Network and Pretrained Models

Read and write Neuroglancer datasets programmatically.

Datasets, Transforms and Utilities specific to Biomedical Imaging

Skeletonize densely labeled 3D image segmentations with TEASAR. (Medial Axis Transform)

Codes that I have written to complete promise12 prostate segmentation competition.

A generalizable application framework for segmentation, regression, and classification using PyTorch

Dijkstra's Shortest Path for 6, 18, and 26-Connected 3D (Volumetric) Image Volumes

Marching Cubes & Mesh Simplification on multi-label 3D images.

Transfer Learning with DCNNs (DenseNet, Inception V3, Inception-ResNet V2, VGG16) for skin lesions classification

Open source Python library for building bioimage analysis pipelines