fitushar

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

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

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-Grad-CAM

This repo contains Grad-CAM for 3D volumes.

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)

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.

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

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.

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

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

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.

SURGERY-ASSISTED-BY-COMPUTER-AND-MEDICAL-ROBOTICS-

This Repository contains all the work done for MAIA 3rd Semester Coursework of SURGERY ASSISTED BY COMPUTER AND MEDICAL ROBOTICS

Automatic_Breast_Region_Extraction_using_python

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

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

Classification of chest CT using caselevel weak supervision

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.

Luna16_Monai_Model_XAI_Project

SeamCarving_Content-Aware-Image-Resizing

The availability of sophisticated source attribution techniques raises new concerns about privacy and anonymity of photographers, activists, and human right defenders who need to stay anonymous while spreading their images and videos. An image can be considered to be a combination of both significant (foreground) objects and some less significant (background) objects. Content aware image resizing (CAIR) algorithm uses the different edge detection methods to segregate the useful objects from the background. When applied to an image, CAIR can resize the image to a very different aspect ratio without destroying the aspect ratio of the useful objects in the image. In this project, we simply implement a content aware image resizing (CAIR) in MATLAB environment. The main idea to implement CAIR is to remove or insert the vertical or horizontal seams (paths of pixel) having the lowest energy. After implanted the Seam Carving Algorithm for Content aware image resizing (CAIR), analysis shows that the implemented seam carving for CAIR can generate more desirable resized images than cropping, resampling, and conventional seam carving techniques.

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

academic-kickstart

Awesome-Foundation-Models-in-Medical-Imaging

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

ChatGPT_Promt_to_code

fitushar.github.io

My Personal Websie

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)

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.

luna16_multi_size_3dcnn

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

Lung-Disease-Classification-with-2D-Multi-channel-Effect-Analysis

Lung diseases classification in 2D using chest CT cases and Analysis the multi-channel effect on classification. This work is been done during summer internship July-Aguest 2018, Duke University Medical Center.

mlcourse.ai

Open Machine Learning Course

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.

node21-noduledetection

Template for nodule detection algorithm for node21 challenge

NoduleNet

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

rank1_node21_detection

UCI-Automobile-Data-Analysis