jennynanap,github

3d-convolutional-network

Alzheimer disease diagnosis by Deeply Supervised 3D Convolutional Network

ad_prediction

Alzheimer's Disease Prediction by using ResNet, AlexNet

advanced-computer-vision-with-tensorflow

My Solutions to the course Advanced-Computer-Vision-with-Tensorflow

alzheimers_disease_progression

Code for analysis of ADNI data

brain-tumor-detection-and-segmentation-using-deep-learning

This repo includes Glioma Segmentation with Mask R-CNN and U-Net.

brain-tumor-detection-from-mri-images-spring-2018

Earlier detection of brain tumors plays a vital role in its treatment as well as dynamically increase the survival rate of the patients. Magnetic Resonance Imaging (MRI) scans are widely used to diagnose the brain tumors which provides better accuracy than other medical imaging techniques. Still, the manual segmentation of MRI images and detecting the brain tumors is a time consuming and prone to error task, which is currently done by the medical experts or radiologists. So, there is an evident necessity for automatic brain tumor segmentation and extracting various characteristics of brain tumors. In this study, three widely used standard image segmentation methods (threshold based, k-means clustering and watershed segmentation) has been tested using collected brain MRI images to isolate the tumors from the rest of the brain regions, and their performance was compared based on the segmentation output. K-means clustering showed a better result than two other methods. Besides this, a graphical user interface (GUI) is designed based on primary image processing techniques and by using the solidity feature of brain tumors. Two of the highly useful brain tumor characteristics (area, and perimeter) are also measured here and displayed on the output window of GUI. The accuracy of this application for tumor detection on brain MRI images and features calculation is much high. More features can be extracted, and the accuracy can be maximized by following some other rigorous techniques, which later could be highly helpful for the medical practitioners working in this field.

catheter_detection

Automatic catheter detection in pediatric X-ray images using a scale-recurrent network and synthetic data

ceal-medical-image-segmentation

Active Deep Learning for Medical Imaging Segmentation

chexnet-with-localization

Weakly Supervised Learning for Findings Detection in Medical Images

clinical_notes_classification

To interpret healthcare data in the plain text format so that the meaningful patient health information in these plain text clinical notes can be automatically extracted and clinically actionable values can derived out of it.

cnn-interpretability

🏥 Visualizing Convolutional Networks for MRI-based Diagnosis of Alzheimer’s Disease

cnn-rnn-a-unified-framework-for-multi-label-image-classification

Paper implementation

cnn-rnn-classifier

A practical example on how to combine both a CNN and a RNN to classify images.

cnnbasedmedicalsegmentation

Code for reproducing the results of our paper on CNN-based medical image segmentation

coursera-ai-for-medicine-specialization

Programming assignments, labs and quizzes from all courses in the Coursera AI for Medicine Specialization offered by deeplearning.ai

courses

fast.ai Courses

covid-19-detection

The implementation of "A Weakly-supervised Framework for COVID-19 Classification and Lesion Localization from Chest CT"

covid-19_prognosis

An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department

covid-net

COVID-Net Open Source Initiative

data_science_for_all

Code and resources for my blog and articles to share Data Science and AI knowledge and learnings with everyone

deep-learning-for-image-processing

deep learning for image processing including classification and object-detection etc.

deepsleepnet

DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG

deformable-detr

Deformable DETR: Deformable Transformers for End-to-End Object Detection.

detr

End-to-End Object Detection with Transformers

diagnosis_covid19

OpenCovidDetector is an opensource COVID-19 diagnosis system implementing on pytorch, which is also as presented in our paper: Development and evaluation of an artificial intelligence system for COVID-19 diagnosis. Nat Commun 11, 5088 (2020).(https://doi.org/10.1038/s41467-020-18685-1)

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