ShahariarRabby

awesome-chatgpt-prompts

This repo includes ChatGPT prompt curation to use ChatGPT better.

Summer2024-Internships

Collection of Summer 2024 tech internships!

netron

Visualizer for neural network, deep learning and machine learning models

PlotNeuralNet

Latex code for making neural networks diagrams

ng-video-lecture

MedSAM

Segment Anything in Medical Images

FAANG-Coding-Interview-Questions

A curated List of Coding Questions Asked in FAANG Interviews

ChatGPT-Paper-Reader

This repo offers a simple interface that helps you to read&summerize research papers in pdf format. You can ask some questions after reading. This interface is developed based on openai API and using GPT-3.5-turbo model.

SAM4MIS

Segment Anything Model for Medical Image Segmentation: paper list and open-source project summary

xrnerf

OpenXRLab Neural Radiance Field (NeRF) Toolbox and Benchmark

visualkeras

Visualkeras is a Python package to help visualize Keras (either standalone or included in TensorFlow) neural network architectures. It allows easy styling to fit most needs. This module supports layered style architecture generation which is great for CNNs (Convolutional Neural Networks), and a graph style architecture, which works great for most models including plain feed-forward networks.

DeepLung

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

HybridSN

A keras based implementation of Hybrid-Spectral-Net as in IEEE GRSL paper "HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification".

3D-Medical-Imaging-Preprocessing-All-you-need

This Repo Will contain the Preprocessing Code for 3D Medical Imaging

mednerf

SAM-Medical-Imaging

FullyConvolutionalTransformer

Official implementation of The Fully Convolutional Transformer for Medical Image Segmentation

Radiomics-Features-Extractor

Hand-crafted radiomics and deep learning-based radiomcis features extraction.

ESCC_ML

Deep-learning Radiomics for Classification Modelling

uniformizing-3D

[MICCAI'2020 PRIME] Uniformizing Techniques to Process CT scans with 3D CNNs for Tuberculosis Severity Estimation.

MCNN-based_HSI_Classification

MCNN-CP：Hyperspectral Image Classification Using Mixed Convolutions and Covariance Pooling (TGARS 2021)； Oct-MCNN-HS：3D Octave and 2D Vanilla Mixed Convolutional Neural Network for Hyperspectral Image Classification With Limited Samples (Remote Sensing, 2021)

Summer-2024-internship

List of summer internship in 2024

3DUnet_tensorflow2.0

This Repo is for implementation of 3D unet in Tensorflow 2.0v

MSR-3DCNN

This is the code of the paper Multiple Spectral Resolution 3D Convolutional Neural Network for Hyperspectral Image Classification. And the paper has been accpeted by remote sensing.

3DGAN-ViT

Here is the code developed for the paper "A deep learning framework based on generative adversarial networks and vision transformer for complex wetland classification using limited training samples" puplished in International Journal of Applied Earth Observation and Geoinformation.

HSI_classification-via-2D-3D-Conv-and-Hybrid-Net

HSI classification, 2D,3D Conv, Hybrid Net, GAN

Study-of-Low-dose-to-High-dose-CT-using-Supervised-Learning-with-GAN-and-Virtual-Imaging-Trials

Computed tomography (CT) is one of the most widely used radiography exams worldwide for different diagnostic applications. However, CT scans involve ioniz- ing radiational exposure, which raises health concerns. Counter-intuitively, low- ering the adequate CT dose level introduces noise and reduces the image quality, which may impact clinical diagnosis. This study analyzed the feasibility of using a conditional generative adversarial network (cGAN) called pix2pix to learn the mapping from low dose to high dose CT images under different conditions. This study included 270 three-dimensional (3D) CT scan images (85,050 slices) from 90 unique patients imaged virtually using virtual imaging trials platform for model development and testing. Performance was reported as peak signal-to-noise ra- tio (PSNR) and structural similarity index measure (SSIM). Experimental results demonstrated that mapping a single low-dose CT to high-dose CT and weighted two low-dose CTs to high-dose CT have comparable performances using pix2pix CGAN and applicability of using VITs

Lung-CT-fastai-2020

Code to reproduce the results in "Pulmonary nodule classification in lung cancer from 3D thoracic CT scans"

BraTS-MGMT-Classification

NeRF-in-Colab

You can create nerf GIFs with notebook from this repository