biomedical-image-processing

Tunable U-Net implementation in PyTorch

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

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

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

ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection

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

Datasets, Transforms and Utilities specific to Biomedical Imaging

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

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

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

Read and write Neuroglancer datasets programmatically.

PyTorch Connectomics: segmentation toolbox for EM connectomics

Skeletonize densely labeled 3D image segmentations with TEASAR.

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

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

Rank3 Code for ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection, Task 3

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

Smart India Hackathon 2019 project given by the Department of Atomic Energy

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

Mesh-based Monte Carlo (MMC)

Tools for computational pathology

WMH segmentaion with unet, dilated_unet, and with ideas from denseNet

Remap, mask, renumber, unique, and in-place transposition of 3D labeled images. Point cloud too.

Deep Learning approaches for EM data

Cancer Detection from Microscopic Images by Fine-tuning Pre-trained Models ("Inception") for new class labels

Segmenting WSIs using Deep Convolutional Neural Networks

Complete U-net Implementation with keras

Binary Pattern Dictionary Learning for gene activation in microscopy images

A brain MRI segmentation tool that provides accurate robust segmentation of problematic brain regions across the neurodegenerative spectrum. The methodology is generalisable to perform well with the typical variance in MRI acquisition parameters and other factors that influence image contrast.