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brats2018-graphlabunn

brats20_unet3d_autoencoder

3d unet and 3d autoencoder for automatical segmentation and feature extraction.

c3d

C3D is a modified version of BVLC caffe to support 3D ConvNets.

caffe

Caffe: a fast open framework for deep learning.

capstone_whitematterml

Interpretable machine learning approach to identifying white matter brain differences between healthy aging and Alzheimer’s onset through diffusion tensor imaging



captk

Cancer Imaging Phenomics Toolkit (CaPTk) is a software platform to perform image analysis and predictive modeling tasks. Documentation: https://cbica.github.io/CaPTk/

centernet

Object detection, 3D detection, and pose estimation using center point detection:

chd-detect

classification_models

Classification models trained on ImageNet. Keras.

cnn-explainer

Learning Convolutional Neural Networks with Interactive Visualization.

cnn-using-tensorflow2

covid19_classification_tmempr

Medical images are crucial data sources for not easily diagnosed diseases. X-rays, one of the medical images, have high resolution. Processing high-resolution images leads to a few problems such as the difficulties in data storage, the computational load, and the time required to process high-dimensional data. It is a vital element to be able to diagnose diseases fast and accurately. In this study, a data set consisting of lung X-rays of patients with and without COVID-19 symptoms was taken into consideration and disease diagnosis from these images can be summarized in 2 steps as preprocessing and classification. Preprocessing step is the feature extraction process and in this step, the recently developed decomposition-based method Tridiagonal Matrix Enhanced Multivariance Products Representation (TMEMPR) is proposed as a feature extraction method. Classification of images is the second step where the Random Forest and Support Vector Machine (SVM) is applied as classifiers. Also, X-ray images have been reduced by 99,9\% with TMEMPR and with several state-of-the-art feature extraction methods which are Discrete Wavelet Transform (DWT), Discrete Cosine Transform(DCT) The results are examined under different feature extraction methods. It is observed that a higher accuracy rate of classification is achieved by using the TMEMPR method.

cp-flow

Convex potential flows

csci480j-appliedmachinelearning

Coursework for NIU's CSCI480J - Applied Machine Learning course, Spring 2020 Semseter

data-science-cheatsheet

data-science-complete-tutorial

For extensive instructor led learning

data-science-ml-full-stack-2022

Everything you need to know for data science.

dc-unet

We proposed a novel U-Net-based model -- DC-UNet to do medical image segmentation.

deep-learning-for-medical-applications

Deep Learning Papers on Medical Image Analysis

deep-learning-with-tensorflow

deep-radiomics-glioma

Source codes for "Observing deep radiomics for the classification of glioma grades."

deeplearningframeworks

Demo of running NNs across different frameworks

deepmrseg

deepseg

DeepSeg: Deep Neural Network Framework for Automatic Brain Tumor Segmentation using Magnetic Resonance FLAIR Images

deeptract

Official implementation of the paper "DeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractography" (by Benou and Riklin-Raviv): https://arxiv.org/abs/1812.05129. Any use of this code requires a citation of the paper.

detectron

FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.

dwi-inpainting

Reconstructing cropped data in 3D diffusion MRI images using variational autoencoders

dynamic

EchoNet-Dynamic is a deep learning model for assessing cardiac function in echocardiogram videos.

easymocap

Make human motion capture easier.

ecg_mit

数据集使用mit-hib数据库 使用2维卷积神经网络实现八种心率不齐的诊断