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xinliyangguang's Projects

ai icon ai

This is a knowledge base I am collecting during my AI journey including Abbreviations, Papers, Platforms and Blogs

brain-segmentation icon brain-segmentation

Deep learning based skull stripping and FLAIR abnormality segmentation in brain MRI using U-Net

correctivespinalsurgery icon correctivespinalsurgery

This project performs exploratory analysis on medical data and predicts chances of the corrective surgery being successful using random forests in Python.

deepmedic icon deepmedic

Efficient Multi-Scale 3D Convolutional Neural Network for Segmentation of 3D Medical Scans

diabetic-patients-readmission-prediction icon diabetic-patients-readmission-prediction

Diabetes is a medical condition that is caused due to insufficient production and secretion of insulin from the pancreas in case of Type-1 diabetes and defective response of insulin Type-2 diabetes. Diabetes is one of the most prevalent medical conditions in people today Hospital readmission for diabetic patients is a major concern in the United States. Over $250 million dollars was spent on treatment of readmitted diabetic inpatients in 2011 alone. Diabetes is chronic and does not have any specific cure. Objective:- Hospital readmission rates for certain conditions are now considered an indicator of hospital quality, and also affect the cost of care adversely. Hospital readmissions of diabetic patients are expensive as hospitals face penalties if their readmission rate is higher than expected and reflects the inadequacies in health care system. For these reasons, it is important for the hospitals to improve focus on reducing readmission rates. Identify the key factors that influence readmission for diabetes and to predict the probability of patient readmission. Approach:- The dataset chosen is that available on the UCI website which contains the patient data for the past 10 years for 130 hospitals. The code has been written in Python using different libraries like scikit-learn, seaborn, matplotlib etc. Different machine learning techniques for classification and regression like Logistic regression, Random forest etc have been used to achieve the objective. Keywords: Machine Learning, Python, scikit-learn, EDA, Healthcare

effective-treatment-for-diabetes icon effective-treatment-for-diabetes

Evaluated the efficiency of insulin-based treatments for diabetes patients. Built a prediction model to recommend solo insulin treatment or conjunction of drugs for a new patient by understanding the past medical history and characteristics like age, weight. Key skills: Python, Feature Engineering, Decision Trees, Random Forest, Naive Bayes, KNN, Logistic Regression, Ensemble Techniques.

lstm-crf-medical icon lstm-crf-medical

构建医疗实体识别的模型，包含词典和语料标注，基于python构建

medical-data-mining icon medical-data-mining

Data Mining Analysis of mechanisms- based classification of musculoskeletal pain in clinical practice. K-means, logistic regression, random forests, all the good stuff

medical-diagnosis-system icon medical-diagnosis-system

This is an offline application which predicts the possible diseases a patient can have based on the given symptoms. This uses the algorithms Naive Bais, Decision Tree, Random Forest, Bayes Theorem, etc.

medicalnet icon medicalnet

Many studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project provides a series of 3D-ResNet pre-trained models and relative code.

unetplusplus icon unetplusplus

Official Keras Implementation for UNet++ in IEEE Transactions on Medical Imaging and DLMIA 2018

vnet-tensorflow icon vnet-tensorflow

Implementation of vnet in tensorflow for medical image segmentation

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