🔮 My Personal Open Source'rer Profile

This project focuses on using thermal imaging as a non-invasive method for detecting breast cancer. Repository for academic purposes.

Repository for method to analyse the relationship between germline variants and somatic mutations and alternative splicing in breast cancer patients based on RNA-Seq data,

Breast Cancer Pattern Recognition through Association Rule Mining

This repository contains implementations and models related to the semantic segmentation of breast images. The main goal is to apply fine-tuning to the Semantic-SAM model to enhance accuracy in the segmentation of mammary structures(breast).

Medical Image processing project.

This project provides the classification of DNA sequences for Breast cancer prediction which into promoter regions associated. Using machine learning and deep learning techniques, I analyze and try to predict sequence data for negative and positive answers in cancer prediction.

Breast Cancer H&E classification of Images and Image Generation

The Breast Radar-based Image Quality Analysis (BRIQS) Framework was created as part of a Master's Year (MAI Biomedical Engineering) Project at Trinity College Dublin. BRIQS is a free and open-source framework for Microwave Radar-based Imaging. It builds upon the BRIGID phantom dataset and MERIT software.

NTU Deep Learning Medical Image course

Segmentation of Breast Cancers using various segmentation loss functions

This project develops a machine learning-based onsite health diagnostic system, facilitating real-time analysis and early detection of health conditions. By integrating data from various sources, it offers personalized insights and enhances healthcare accessibility.

Histomic Prognostic Signature (HiPS): A population-level computational histologic signature for invasive breast cancer prognosis

A web app to demonstrate the usage of Wasm-iCARE to calculate the absolute risk of breast cancer.

Predicting breast cancer survival using machine learning models

Breast Cancer Prediction with Hybrid Filter-Wrapper Feature Selection

Scikit-Learn Supervised Machine Learning for Breast Cancer Binary Classification

✨ [AVI 2020] A prototype platform for lesion annotations and manual segmentation on breast cancer diagnosis with a multimodality strategy. The work was presented in the Advanced Visual Interfaces (AVI) conference.

This repository contains one of my Google Sheet files and a conference paper that has been accepted in ISPA IEEE, The Sheet used for organizing research papers related to breast cancer analysis. The focus of the papers is on the utilization of clinical datasets and machine/deep learning techniques. The collection spans the period from 2020 to 2023.

A repository that contains all the code for the interactive Shiny app of the models developed in our work on predicting response to neoadjuvant treatment.