Pinned Repositories
Adaptive_t-vMF_Dice_loss
AutoEncoders
This repository explores the use of autoencoders for breast cancer detection using ultrasound image data.
bank_marketing
BnB_BFS
In this repository, I performed Breadth-First Search (BFS) on the Branch and Bound algorithm step by step.
Breast-Ultrasound-Image-Analysis
This repository contains a Jupyter notebook that demonstrates various tasks related to breast ultrasound image analysis using deep learning techniques. The notebook combines code for image segmentation, classification, compression, reconstruction, and generation.
Breast_cancer_image_segmentation
This project leverages the power of U-Net architecture implemented in PyTorch for breast cancer image segmentation.
DDM2
[ICLR2023] Official repository of DDM2: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models
ECG_analysis
In this repository, ECG data (electrocardiogram) has been processed and classified with Tensorflow and Pytorch.
GAN_BreastCancerData_Pytorch
This repository contains a Generative Adversarial Network (GAN) implementation for generating synthetic breast cancer images using PyTorch. The GAN is trained on a dataset of breast cancer images.
TSP_optimization
This repository contains a Python notebook implementing a class for solving multiple Traveling Salesman Problems (TSP) using Pyomo and the CPLEX solver. The class includes a solution for the simple TSP scenario when there is only one driver.
parsakhavarinejad's Repositories
parsakhavarinejad/Breast_cancer_image_segmentation
This project leverages the power of U-Net architecture implemented in PyTorch for breast cancer image segmentation.
parsakhavarinejad/ECG_analysis
In this repository, ECG data (electrocardiogram) has been processed and classified with Tensorflow and Pytorch.
parsakhavarinejad/TSP_optimization
This repository contains a Python notebook implementing a class for solving multiple Traveling Salesman Problems (TSP) using Pyomo and the CPLEX solver. The class includes a solution for the simple TSP scenario when there is only one driver.
parsakhavarinejad/Adaptive_t-vMF_Dice_loss
parsakhavarinejad/AutoEncoders
This repository explores the use of autoencoders for breast cancer detection using ultrasound image data.
parsakhavarinejad/bank_marketing
parsakhavarinejad/BnB_BFS
In this repository, I performed Breadth-First Search (BFS) on the Branch and Bound algorithm step by step.
parsakhavarinejad/Breast-Ultrasound-Image-Analysis
This repository contains a Jupyter notebook that demonstrates various tasks related to breast ultrasound image analysis using deep learning techniques. The notebook combines code for image segmentation, classification, compression, reconstruction, and generation.
parsakhavarinejad/DDM2
[ICLR2023] Official repository of DDM2: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models
parsakhavarinejad/GAN_BreastCancerData_Pytorch
This repository contains a Generative Adversarial Network (GAN) implementation for generating synthetic breast cancer images using PyTorch. The GAN is trained on a dataset of breast cancer images.
parsakhavarinejad/heart-disease-classification-clustering
A machine learning project for the classification and clustering of heart disease using the heart.csv dataset. Explore various algorithms to predict heart disease presence and group individuals based on similar characteristics.
parsakhavarinejad/Made-With-ML
parsakhavarinejad/Pairs-Trading-With-Python
parsakhavarinejad/parsakhavarinejad
About Me
parsakhavarinejad/parsakhavarinejad.github.io
Website
parsakhavarinejad/Real-ESRGAN-Streamlit
This repository is a fork of the Real-ESRGAN project (https://github.com/ai-forever/Real-ESRGAN) with an additional feature for local image super-resolution. It leverages Streamlit to create a user-friendly web application that allows you to upscale images directly on your machine.
parsakhavarinejad/Transfer_learning_finetuning_BreastCancerData
This repository contains a pre-trained image classification model utilizing the VGG16, VGG19 and EfficientNet-B7 architecture. The model supports transfer learning and fine-tuning, offering flexibility for adapting to specific image recognition tasks.
parsakhavarinejad/transfer_learning_ResNet50
This notebook demonstrates transfer learning using the ResNet50 architecture on the oxford_flowers_102 dataset.
parsakhavarinejad/unet_on_oxford_pet_dataset
This project implements U-Net, a convolutional neural network architecture, on the Oxford Pets III dataset.