Orange714

Orange714's Stars

• venkatramnank/BRATS2021

  Curriculum Learning for Brain Tumor Segmentation

  Language:Jupyter Notebook31

• OSUPCVLab/SegFormer3D

  Official Implementation of SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation (CVPR2024)

  Language:Python3910

• JonathanE42/BraTS2021

  Brain Tumor Segmentation Challenge

  Language:Jupyter Notebook3

• Gunale0926/SORSA

  SORSA: Singular Values and Orthogonal Regularized Singular Vectors Adaptation of Large Language Models

  Language:Python2

• microsoft/AI-System

  System for AI Education Resource.

  Language:Python3.1k393

• tangyudi/Ai-Learn

  人工智能学习路线图，整理近200个实战案例与项目，免费提供配套教材，零基础入门，就业实战！包括：Python，数学，机器学习，数据分析，深度学习，计算机视觉，自然语言处理，PyTorch tensorflow machine-learning,deep-learning data-analysis data-mining mathematics data-science artificial-intelligence python tensorflow tensorflow2 caffe keras pytorch algorithm numpy pandas matplotlib seaborn nlp cv等热门领域

  8.9k2.1k

• Balasingham-AI-Group/Survival_CTplusClinical

  Multimodal Deep Learning for Personalized Renal Cell Carcinoma Prognosis: Integrating CT Imaging and Clinical Data

  11

• edaaydinea/OP2-Prediction-of-the-Different-Progressive-Levels-of-Alzheimer-s-Disease-with-MRI-data

  This is an optional model development project on a real dataset related to predicting the different progressive levels of Alzheimer’s disease (AD) with MRI data.

  Language:Jupyter Notebook71

• edaaydinea/OP1-Prediction-of-the-Different-Progressive-Levels-of-Alzheimer-s-Disease

  This is an optional model development project on a real dataset related to predicting the different progressive levels of Alzheimer’s disease (AD).

  Language:Jupyter Notebook84

• tsyoshihara/Alzheimer-s-Classification-EEG

  Alzheimer’s Disease (AD) is the most common neurodegenerative disease. It is typically late onset and can develop substantially before diagnosable symptoms appear. Electroencephalogram (EEG) could potentially serve as a noninvasive diagnostic tool for AD. Machine learning can be helpful in making inferences about changes in frequency bands in EEG data and how these changes relate to neural function. The EEG data was sourced from 2014 paper titled Alzheimer’s disease patients classification through EEG signals processing by Fiscon et al. There were patients with AD, mild cognitive impairment (MCI), and healthy controls. The data was already preprocessed using a fast fourier transform (FFT) to take the data from the time domain to the frequency domain. There were differing levels of effectiveness in terms of classification but generally, Fisher’s discriminant analysis (FDA), relevance vector machine, and random forest approaches were most successful. Due to inconsistent feature importances in different models, conclusions about important frequency bands for classification were not able to be made at this time. Similarly, different frequencies were not able to be localized to different regions of the brain. Further research is necessary to develop more interpretable models for classification.

  Language:Python2710

• ipis-mjkim/caueeg-ceednet

  This repository is the official implementation of "Deep learning-based EEG analysis to classify mild cognitive impairment for early detection of dementia: algorithms and benchmarks" from the CNIR (CAU NeuroImaging Research) team.

  Language:Jupyter Notebook187

• facebookresearch/demucs

  Code for the paper Hybrid Spectrogram and Waveform Source Separation

  Language:Python7.9k983

• neheller/kits19

  The official repository of the 2019 Kidney and Kidney Tumor Segmentation Challenge

  Language:Python422178

• iguanaus/ScatterNet

  Code for all work presented in Nanophotonic Particle Simulation and Inverse Design Using Artificial Neural Networks

  Language:MATLAB7836

• histocartography/hact-net

  Language:Python7113

• BiomedicalMachineLearning/HEMnet

  A neural network software for using Molecular labelling to improve pathological annotation of H and E tissues

  Language:Jupyter Notebook297

• kasesa/Allergic-Rhinitis

  1

• smilelight/lightKG

  基于Pytorch和torchtext的知识图谱深度学习框架。

  Language:Python610149

• amusi/Deep-Learning-Interview-Book

  深度学习面试宝典（含数学、机器学习、深度学习、计算机视觉、自然语言处理和SLAM等方向）

  7.1k1.3k

• jel-lambda/new-Transweather

  new-Transweather code with proper functioning

  Language:Python122

• jeya-maria-jose/TransWeather

  Pytorch Code for the paper TransWeather - CVPR 2022

  Language:Python15339

• mihirvador/Diagnosing-Multiple-Sclerosis-Using-Machine-Learning

  Analzying 3D MRI scans and diagnosing Multiple Sclerosis from them.

  Language:Python71

• Orange714/RSANet

  RSANet: Recurrent Slice-wise Attention Network for Multiple Sclerosis Lesion Segmentation (MICCAI 2019)

  1

• rsinghlab/GNN-Tumor-Seg

  Using Graph Neural Networks to Segment MRIs of Brain Tumors

  Language:Python154

• ZhoulabCPH/OCDPI

  Language:Python21

• mohaEs/ML4VisAD

  Machine Learning for the Visualization of Alzheimer's Disease

  Language:Python21

• drpredict/DeepDR_Plus

  Language:Python286

• PRITHVIRAJ08/Length-Breadth-and-angle-of-an-object-using-opencv

  Detects objects and it's length, breadth and angle using opencv.

  Language:Jupyter Notebook1

• DrSkippy/Data-Science-45min-Intros

  Ipython notebook presentations for getting starting with basic programming, statistics and machine learning techniques

  Language:Jupyter Notebook1.6k477

• tyb311/SkelCon

  PyTorch implementation for our paper on TMI2022: Retinal Vessel Segmentation with Skeletal Prior and Contrastive Loss

  Language:Python495