Compares the signals from different regions of interest (ROIs) to determine which ROIs belong to the same cell. Used for glioblastoma images.
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Updated
Jul 4, 2024 - Jupyter Notebook
Compares the signals from different regions of interest (ROIs) to determine which ROIs belong to the same cell. Used for glioblastoma images.
This repository contains the code for my MSc project titled "Investigating therapy-driven changes in isoform expression in glioblastoma"
Statistical testing for GBM Margin/ Primary tumours
Glioblastoma tumour classfication and tumour grade segmentattion using U-NET CNN
A fast C++ based implementation of the CA-PPMx by Chandra et al. (2023+).
Accurate Early Detection of GBM Brain Cancer With Deep Learning (AI) - Silver Medal Finalist at GVRSF 2019
Automating exosome-based glioblastoma diagnosis through bioinformatics and machine learning
This repository contains Matlab codes developed for the thesis of the exam of Mathematical Models for Biomedicine, a.y. 2022-23, Master of Science in Mathematical Engineering at Politecnico di Torino, held by proff. Chiara Giverso, Luigi Preziosi, Luca Mesin. This work had been developed in cooperation with Lorenzo Vito Dal Zovo and Enrico Ortu.
Glioblasted is a machine learning model to detect glioblastoma, a high-grade, aggressive form of brain and/or spine cancer.
Image process framework to easily analyse fluorescent glioblastoma cells in pattern of neurons.
This repository contains primary source code for "Transcriptomic portraits and molecular pathway activation features of spinal intramedullary astrocytomas" manuscript.
https://doi.org/10.5281/zenodo.6941367 - Spatiotemporal-Aware Glioblastoma Multiforme Tumor Growth Modeling with Deep Encoder-Decoder Networks
NucleiJ is a Java-based application that supports research into glioblastoma, a fast-growing and aggressive brain tumour. Cross-sectional images are analysed automatically using image processing.
Source code for "Overexpression of CRNDE in glioblastoma is a poor survival prognosis biomarker" paper
Glioblastoma multiforme (GBM) biomarker knowledge base
Reference code for "Improved Prediction of Surgical Resectability in Patients with Glioblastoma using an Artificial Neural Network"
The work presented explains how to segment the brain tumour area in absence of interaction with user basing his technique on a saliency map constructed from three different resonance techniques.
Gene signature analysis of TCGA glioblastoma data
Methods for training and interpreting deep radiogenomic neural networks
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