zmasoomi Goto Github PK

api

awesome-public-datasets

A topic-centric list of HQ open datasets.

brain-segmentation

Deep learning based skull stripping and FLAIR abnormality segmentation in brain MRI using U-Net

cbam-tensorflow

CBAM implementation on TensowFlow

cbam_cnn_architectures_image_classification

Spatial and Channel Attention in CNN Architectures for Image Classification task

keras

Deep Learning for humans

masoomikhanghah

ml

object-detection

public-apis

A collective list of free APIs

rdd

Python tools for regression discontinuity designs

roadmap-to-qml

This repository will contain the major papers, books and blog posts on QML

segthor19-using-resu-net

The recent advances in the field of computer vision has led to the wide use of Convolutional Neural Networks (CNNs) in organ segmentation of computed tomography (CT) images. Image guided radiation therapy requires the accurate segmentation of organs at risk (OARs). In this paper, we propose a 2D U-Net network to automatically segment thoracic organs at risk in computed tomography (CT) images. The architecture consists of a down sampling path to capture features and a symmetric up sampling path to obtain precise localization. SegTHOR19 is a competition timed to the conference IEEE ISBI 2019 that addresses the problem of organs at risk segmentation in Computed Tomography (CT) images. In the SegTHOR19 challenge, 40 CT scans with 4 thoracic organs (i.e., esophagus, heart, trachea and aorta) were used for training [1]. We experimented with both 2D U-net and 2D U-Net with Resnet18 architecture to train the networks. Our best results were obtained by using 2D Convolutional U-Net with ResNet18.

senet

Squeeze-and-Excitation Networks

yolov6

YOLOv6: a single-stage object detection framework dedicated to industrial applications.

zeinab_masoomi

zmasoomi

Config files for my GitHub profile.

zmasoomikhanghah