fitushar

3D-Grad-CAM

This repo contains Grad-CAM for 3D volumes.

3D-GuidedGradCAM-for-Medical-Imaging

This Repo containes the implemnetation of generating Guided-GradCAM for 3D medical Imaging using Nifti file in tensorflow 2.0. Different input files can be used in that case need to edit the input to the Guided-gradCAM model.

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

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

3DUnet_tensorflow2.0

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

AI-in-Lung-Health-Benchmarking-Detection-and-Diagnostic-Models-Across-Multiple-CT-Scan-Datasets

This study presents the development and validation of AI models for both nodule detection and cancer classification tasks. This benchmarking across multiple datasets establishes the DLCSD as a reliable resource for lung cancer AI research.

Brain-Tissue-Segmentation-Using-Deep-Learning-Pipeline-NeuroNet

This Repository is for the MISA Course final project which was Brain tissue segmentation. we adopt NeuroNet which is a comprehensive brain image segmentation tool based on a novel multi-output CNN architecture which has been trained and tuned using IBSR18 dataset

CVIT_ReviCOVID19

Skin-lesion-Segmentation-using-grabcut

Skin lesion segmentation is one of the first steps towards automatic Computer-Aided Diagnosis of skin cancer. Vast variety in the appearance of the skin lesion makes this task very challenging. The contribution of this paper is to apply a power foreground extraction technique called GrabCut for automatic skin lesion segmentation in HSV color space with minimal human interaction. Preprocessing was performed for removing the outer black border. Jaccard Index was measured to evaluate the performance of the segmentation method. On average, 0.71 Jaccard Index was achieved on 1000 images from ISIC challenge 2017 Training Dataset.

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

WeaklySupervised-3D-Classification-of-Chest-CT-using-Aggregated-MultiResolution-Segmentation-Feature

This Repo contains the updated implementation of our paper "Weakly supervised 3D classification of chest CT using aggregated multi-resolution deep segmentation features", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 1131408 (16 March 2020)

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

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

fitushar/3D-GuidedGradCAM-for-Medical-Imaging

This Repo containes the implemnetation of generating Guided-GradCAM for 3D medical Imaging using Nifti file in tensorflow 2.0. Different input files can be used in that case need to edit the input to the Guided-gradCAM model.

fitushar/3D-Grad-CAM

This repo contains Grad-CAM for 3D volumes.

fitushar/WeaklySupervised-3D-Classification-of-Chest-CT-using-Aggregated-MultiResolution-Segmentation-Feature

This Repo contains the updated implementation of our paper "Weakly supervised 3D classification of chest CT using aggregated multi-resolution deep segmentation features", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 1131408 (16 March 2020)

fitushar/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

fitushar/3DCNNs_TF2Modelhub

Almost all the deeplearning libraries provide ready to use 2D models with/without imagenet weights, But In the case of 3D, CNN models are not as available. This repo will contain commonly used 2D CNNs 3D implementations.

fitushar/multi-label-weakly-supervised-classification-of-body-ct

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.

fitushar/3D-Attention-in-tf2--Position-Channel-attention

This repo contains the 3D implementation of the commonly used attention mechanism for imaging.

fitushar/multi-label-annotation-text-reports-body-CT

There is progress to be made in building artificially intelligent systems to detect abnormalities that are not only accurate but can handle the true breadth of findings that radiologists encounter in body (chest, abdomen, and pelvis) Computed Tomography (CT). Currently, the major bottleneck for developing multi-disease classifiers is a lack of manually annotated data. The purpose of this work was to develop high throughput multi-label annotators for body CT reports that can be applied across a variety of abnormalities, organs, and disease states thereby mitigating the need for human annotation.

fitushar/AI-in-Lung-Health-Benchmarking-Detection-and-Diagnostic-Models-Across-Multiple-CT-Scan-Datasets

This study presents the development and validation of AI models for both nodule detection and cancer classification tasks. This benchmarking across multiple datasets establishes the DLCSD as a reliable resource for lung cancer AI research.

fitushar/Automatic_Breast_Region_Extraction_using_python

This Repo will contain Python Implementation of an Automatic approach to Extraction Breast Region from Memogram

fitushar/Image_pixel_recovery_with_lesso_regression

The objective of this mini-project is to Recover a full image from a small number of sampled pixels (compressed sensing). Although the primary goal of this project is to understand and explore the application of regularized. In the process of recovering image pixel using regularized regression, we will explore different concepts and their understanding as following: Understanding how regression can be applied in 2D image analysis domain. Understanding of the discrete cosine transforms (DCT) to define an image in a frequency domain. Explore the importance and application of cross validation in model tunning and hyper-parameter selections. Understanding the impact of applying filtering approach such as median filter on reconstructed image Finally, quantitively evaluating the quality of removed image.

fitushar/Classification-of-chest-CT-using-caselevel-weak-supervision

Classification of chest CT using caselevel weak supervision

fitushar/Improved-Regularization-of-Convolutional-Neural-Networks

To evaluate the performance of each regularization method (cutout, mixup, and self-supervised rotation predictor), we apply it to the CIFAR-10 dataset using a deep residual network with a depth of 20 (ResNet20)

fitushar/Luna16_Monai_Model_XAI_Project

fitushar/CVIT_ReviCOVID19

fitushar/3D-Data-Augmentation-Using-Tf.data-and-Volumentations-3D

fitushar/academic-kickstart

fitushar/Awesome-Foundation-Models-in-Medical-Imaging

A curated list of foundation models for vision and language tasks in medical imaging

fitushar/ChatGPT_Promt_to_code

fitushar/fitushar.github.io

My Personal Websie

fitushar/LinearSVC-BCIs-MP2

Support Vector machine (SVM) is a well know marginal classifier commonly used in different classification problem for both small and high dimensional data [1]. This project applied the Linear SVM classifier to classify two different EEG datasets acquired under different condition (imaginary and actual) to classify left vs right movement.

fitushar/luna16_multi_size_3dcnn

An implement of paper "Multi-level Contextual 3D CNNs for False Positive Reduction in Pulmonary Nodule Detection"

fitushar/mlcourse.ai

Open Machine Learning Course

fitushar/nnDetection

nnDetection is a self-configuring framework for 3D (volumetric) medical object detection which can be applied to new data sets without manual intervention. It includes guides for 12 data sets that were used to develop and evaluate the performance of the proposed method.

fitushar/node21-noduledetection

Template for nodule detection algorithm for node21 challenge

fitushar/NoduleNet

[MICCAI' 19] NoduleNet: Decoupled False Positive Reduction for Pulmonary Nodule Detection and Segmentation

fitushar/rank1_node21_detection

fitushar/UCI-Automobile-Data-Analysis

fitushar/VLST.github.io