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2nd-place-solution-for-vinbigdata-chest-x-ray-abnormalities-detection

Localization of thoracic abnormalities model based on VinBigData (top 1%)

3dunetcnn

Keras 3D U-Net Convolution Neural Network (CNN) designed for medical image segmentation

bayesiannn_uncertainty

Codes for WCCI paper on uncertainty quantification.

bi-fidelity-vae

Bi-fidelity variational auto-encoder for uncertainty quantification applications

capsnet

Tensorflow implementation of capsule network (CapsNet) for traffic prediction.

capsnet-keras

A Keras implementation of CapsNet in NIPS2017 paper "Dynamic Routing Between Capsules". Now test error ＝ 0.34%.

cnn-3d-images-tensorflow

3D image classification using CNN (Convolutional Neural Network)

convnets-as-gps

Code for "Deep Convolutional Networks as shallow Gaussian Processes"

deepgp_approxep

see https://github.com/thangbui/geepee for a faster implementation

deeplung

WACV18 paper "DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification"

deepuq

Bayesian Uncertainty Quantification by Deep Generative Model

edge-connect

EdgeConnect: Structure Guided Image Inpainting using Edge Prediction, ICCV 2019 https://arxiv.org/abs/1901.00212

edges

Structured Edge Detection Toolbox

elasticdeform

Differentiable elastic deformations for N-dimensional images (Python, SciPy, NumPy).

gaussian_process_time_series

New version of traffic prediction with Gaussian Process

generative_inpainting

DeepFill v1/v2 with Contextual Attention and Gated Convolution, CVPR 2018, and ICCV 2019 Oral

gpflow-inter-domain

Gaussian processes in TensorFlow with modifications to allow inter-domain inducing variables

graph-neural-networks

Library to implement graph neural networks in PyTorch

llm-colosseum

Benchmark LLMs by fighting in Street Fighter 3! The new way to evaluate the quality of an LLM

medical-image-classification-using-deep-learning

Tumour is formed in human body by abnormal cell multiplication in the tissue. Early detection of tumors and classifying them to Benign and malignant tumours is important in order to prevent its further growth. MRI (Magnetic Resonance Imaging) is a medical imaging technique used by radiologists to study and analyse medical images. Doing critical analysis manually can create unnecessary delay and also the accuracy for the same will be very less due to human errors. The main objective of this project is to apply machine learning techniques to make systems capable enough to perform such critical analysis faster with higher accuracy and efficiency levels. This research work is been done on te existing architecture of convolution neural network which can identify the tumour from MRI image. The Convolution Neural Network was implemented using Keras and TensorFlow, accelerated by NVIDIA Tesla K40 GPU. Using REMBRANDT as the dataset for implementation, the Classification accuracy accuired for AlexNet and ZFNet are 63.56% and 84.42% respectively.

mri-abnormalities

Image processing application and dataset for looking for brain edemas. Requires image processing toolbench.

pconv-keras

Unofficial implementation of "Image Inpainting for Irregular Holes Using Partial Convolutions". Try at: www.fixmyphoto.ai

registration-using-cnn-stn-icstn

Implementation of STN (Spatial Transformer Network) and ICSTN (Inverse Compositional Spatial Transformer Networks) in Tensorlayer to predict transformation parameters from 2D images.

selection-via-proxy

shape-classifier-cnn

Shapes Classifier using CNN: Is that image a triangle or a square or a circle using CNN/Deep Learning.

torchxrayvision

TorchXRayVision: A library of chest X-ray datasets and models. Classifiers, segmentation, and autoencoders.

triple-anet

[MICCAI'19] Triple ANet: Adaptive Abnormal-aware Attention Network for WCE Image Classification

vit-tensorflow

Vision Transformer Cookbook with Tensorflow

yolo

Examples and tutorials on using SOTA computer vision models and techniques. Learn everything from old-school ResNet, through YOLO and object-detection transformers like DETR, to the latest models like Grounding DINO and SAM.

yolo-object-detection

YOLO object detection with OpenCV and Python