3d-classification

🔥[IEEE TPAMI 2020] Deep Learning for 3D Point Clouds: A Survey

Code for A GRAPH-CNN FOR 3D POINT CLOUD CLASSIFICATION (ICASSP 2018)

PyTorch implementation of "PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation" https://arxiv.org/abs/1612.00593

The PyTorch re-implement of a 3D CNN Tracker to extract coronary artery centerlines with state-of-the-art (SOTA) performance. (paper: 'Coronary artery centerline extraction in cardiac CT angiography using a CNN-based orientation classifier')

This is the official repository of the original Point Transformer architecture.

Weakly supervised 3D classification of multi-disease chest CT scans using multi-resolution deep segmentation features via dual-stage CNN architecture (DenseVNet, 3D Residual U-Net).

PointHop: An Explainable Machine Learning Method for Point Cloud Classification

PointHop++: A Lightweight Learning Model on Point Sets for 3D Classification

A fast and low memory requirement version of PointHop and PointHop++, which is built upon Apache Spark.

3D Face Classification with Graph Neural Networks

3D MNIST Point Cloud Classifier using 3D ConvNet with Swift for TensorFlow

This package implements deep learning modules for medical imaging application in PyTorch (miTorch).

Solution of team tara: Public 7th, Private 13th (The renewed pipeline scores 8th place)

ModelNet10 classification method based on rendered videos.

A new approach for retrieval and classification of 3D models that directly performs in the CAD format without any format conversion to other representations like point clouds of meshes, thus avoiding any loss of information.

Automated detection of focal cortical dysplasia

Implementation of Multi-view CNN for 3D classification evaluated on MoodleNet40 dataset.

RandAugment process for point cloud data to handle with 3D classification task

A rule-based algorithm enabled the automatic extraction of disease labels from tens of thousands of radiology reports. These weak labels were used to create deep learning models to classify multiple diseases for three different organ systems in body CT.

This repository is for the Deep learning(Classification & XAI) part with 3D OCTA volumes.