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3d-attention-in-tf2--position-channel-attention

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

3d-data-augmentation-using-tf.data-and-volumentations-3d

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

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.

3dunet_tensorflow2.0

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

academic-kickstart

automatic_breast_region_extraction_using_python

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

awesome-foundation-models-in-medical-imaging

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

b31xb-iip-tp-1718

bme-590-machine-learning-in-imaging

This repository is used for Duke BME 590 Machine Learning in Imaging course taught in Spring 2019.

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

brain-tissue-segmentation-using-expectation-maximization

Medical Image Segmentation and Applications (MISA) LAB task.

brain-tumour-cad-lecture-activity

Student Lecture Activity for CAD lecture

cadx-classification-challenge

chatgpt_promt_to_code

classification-of-chest-ct-using-caselevel-weak-supervision

Classification of chest CT using caselevel weak supervision

cyclical-learning-rates-for-training-neural-networks-with-unbalanced-data-sets

As the learning rate is one of the most important hyper-parameters to tune for training convolutional neural networks. In this paper, a powerful technique to select a range of learning rates for a neural network that named cyclical learning rate was implemented with two different skewness degrees. It is an approach to adjust where the value is cycled between a lower bound and upper bound. CLR policies are computationally simpler and can avoid the computational expense of fine tuning with fixed learning rate. It is clearly shown that changing the learning rate during the training phase provides by far better results than fixed values with similar or even smaller number of epochs.

dann_tf2.0

DANN in TensorFlow 2.0

deep-learning-for-java--mass-classification-using-transfer-learning

Mass Detection in Breast Using Transfer Learning for Computer Aided Diagnosis

densevnet3d_chest_abdomen_pelvis_segmentation_tf2

This Repo containes the implemnetation of DenseVent in tensorflow 2.0 for chest-abdomen-pelvis (CAP) Segmentation

design-an-inverse-kinematic-controller-to-move-end-effector-

Design an inverse kinematic controller to move end-effector. This work is done as a coursework for the 2nd semester of MAIA in Introduction to Robotics course.

development-of-a-modular-biopotential-amplifier-trainer-for-biomedical-instrumentation-laboratory-ex

This Repository contains the Circuit design and simulation related to RTR module

dltk

Deep Learning Toolkit for Medical Image Analysis

ehealth_course_work

This repository Contains the Lab works of the Ehealh Courseworks Lab reports and coreponding codes

fitushar.github.io

My Personal Websie

hugo-academic

📝 The website builder for Hugo. Build and deploy a beautiful website in minutes!

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.

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)