Robinzzs

通过反向传播算法实现神经网络和小波神经网络。Implement neural network and wavelet neural network through back-propagation algorithm. Реализация нейронных сетей и вейвлет-нейронных сетей с помощью метода обратного распространения ошибки.

Language:Jupyter NotebookMIT2400

Medical-Segmentation-Decathlon-U-net-CNN-with-Generalized-Dice-Coefficient

With recent advances in machine learning, semantic segmentation algorithms are becoming increasingly general-purpose and translatable to unseen tasks. Many key algorithmic advances in the field of medical imaging are commonly validated on a small number of tasks, limiting our understanding of the generalizability of the proposed contributions. A model which works out-of-the-box on many tasks, in the spirit of AutoML (Automated Machine Learning), would have a tremendous impact on healthcare. The field of medical imaging is also missing a fully open source and comprehensive benchmark for general-purpose algorithmic validation and testing covering a large span of challenges, such as: small data, unbalanced labels, large-ranging object scales, multi-class labels, and multimodal imaging, etc. To address these problems, in this project, as part of the MSD challenge, we propose a generic machine learning algorithm which we applied on two organs: liver and tumors, spleen. We propose an unsupervised generic model by implementing U-net CNN architecture with Generalized Dice Coefficient as loss function and also as a metric. The MSD dataset consists of dozens of medical examinations in 3D (per organ), we’ll transform the 3-dimensional data into 2-d cuts as an input of our U-net. Experimental results show that our generic model based on U-net and Generalized Dice Coefficient algorithm leads to high segmentation accuracy for each organ (liver and tumors, spleen), separately, without human interaction, with a relatively short run time compared to traditional segmentation methods.

Language:Python2100

BLUE-Net-BLUmberg-function-based-Ensemble-Network-for-Liver-and-Tumor-Segmentation-from-CT-scans

Language:Jupyter NotebookGPL-3.0100

liver-tumor-segmentation

在读研究生踩着各种坑摸索着写了一个训练脚本

Language:Python400

3Dircadb_liver_segmentation_keras

Language:Python300

ShellCrash

Run sing-box/mihomo as client in shell

Language:Shell797400

shellclash_docker

在任意Linux主机上, 利用Docker自动创建并配置虚拟OpenWrt路由容器以运行 juewuy's ShellClash 实现旁路由透明代理

Language:Shell17300

LiTS---Liver-Tumor-Segmentation-Challenge

LiTS - Liver Tumor Segmentation Challenge

Language:PythonMIT23100

geoist

An Open-Source Geophysical Python Library for Geoscience Prototype Research

Language:PythonMIT8100

SeismicLab

Matlab research tools to read, write and process seismic data

Language:MATLAB7200

Learning-Scientific_Machine_Learning_Residual_Based_Attention_PINNs_DeepONets

Physics Informed Machine Learning Tutorials (Pytorch and Jax)

Language:Jupyter Notebook41000

Objectron

Objectron is a dataset of short, object-centric video clips. In addition, the videos also contain AR session metadata including camera poses, sparse point-clouds and planes. In each video, the camera moves around and above the object and captures it from different views. Each object is annotated with a 3D bounding box. The 3D bounding box describes the object’s position, orientation, and dimensions. The dataset contains about 15K annotated video clips and 4M annotated images in the following categories: bikes, books, bottles, cameras, cereal boxes, chairs, cups, laptops, and shoes

Language:Jupyter NotebookNOASSERTION222600

mediapipe

Cross-platform, customizable ML solutions for live and streaming media.

Language:C++Apache-2.02641100

3D-BoundingBox

PyTorch implementation for 3D Bounding Box Estimation Using Deep Learning and Geometry

Language:PythonMIT43200