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Search results for deep learning biomedical image processing

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14 search results found

Tools for computational pathology

Pytorch_connectomics ⭐ 151

PyTorch Connectomics: segmentation toolbox for EM connectomics

3dunet Tensorflow Brats18 ⭐ 147

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

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

Isic2018 ⭐ 113

ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection

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

Open source Python library for building bioimage analysis pipelines

Promise12_segmentation ⭐ 66

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

Skin Lesion Recognition.pytorch ⭐ 32

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

Cancer Detection From Microscopic Tissue Images With Deep Learning ⭐ 28

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

Brain Mri Segmentation ⭐ 25

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

Oct Retinal Layer Segmenter ⭐ 18

Wsisegmentation ⭐ 12

Segmenting WSIs using Deep Convolutional Neural Networks

Malaria Detection Using Deep Learning Techniques ⭐ 9

Malaria Parasite Detection using Efficient Neural Ensembles. Malaria, a life threatening disease caused by the bite of the Anopheles mosquito infected with the parasite, has been a major burden towards healthcare for years leading to approximately 400,000 deaths globally every year. This study aims to build an efficient system by applying ensemble techniques based on deep learning to automate the detection of the parasite using whole slide images of thin blood smears.

📎 About MIDA Project

Paint4brains ⭐ 7

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.

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