Code for my MSc Artificial Intelligence dissertation at the University of St Andrews
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Updated
Sep 12, 2021 - Jupyter Notebook
Code for my MSc Artificial Intelligence dissertation at the University of St Andrews
Detection and Extraction of any possibly present tumors in the human brain by processing and analyzing MRI Scans.
Comparing segmentation model on brain segmentation task
Dermatologists suffer from the difficulty of locating cancerous and malignant skin lesions, which causes many problems during the process of removing the tumor, which leads to the return of the tumor again. In determining the location of the tumor and its spread and determining the area that must be removed accurately.
Standard Phantom for Medical 3D printing modeling software evaluation
Predict a bounding box around the heart in X-ray images.
Melanoma Deep Learning Project: Leveraging the power of deep learning for the detection and analysis of melanoma in medical images. This repository features Python and Jupyter Notebook resources aimed at advancing dermatological diagnostics through artificial intelligence
Labelless automated airway measurement using style transfer to generate synthetic data.
My project for the national ai competition in Germany 2023
Captioning model for Medical imaging.
Medical Image Registration Demo
Correlation Between IBSI Morphological Features and Manually-Annotated Shape Attributes on Lung Lesions at CT (MIUA 2022)
Dissertation completed for the award of MSci in Computer Science. This dissertation is about automated breast cancer detection in low-resolution whole-slide pathology images using a deep convolutional neural network pipeline.
Comprehensive Analysis of Domain Transferability Using Deep-Learning-Based Interpretation of Chest Radiographs
SW tool to identify, score and display Windmill Artifact in CT images
MICCAI Challenge 2023: Pubic Symphysis-Fetal Head Segmentation task solved using the U-Net model from the SMP library, accompanied by an algorithm for Angle of Progression estimation.
Official PyTorch implementation of the paper: <Bootstrap Representation Learning for Segmentation on Medical Volumes and Sequences>
IEEE ISBI 2022 paper: CEUSegNet: A Cross-Modality Lesion Segmentation Network for Contrast-Enhanced Ultrasound.
Official repository of "Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Models"
SAM Adaptation for mp-MRI Brain Tumor Segmentation
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