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.
Software for automatic segmentation and generation of standardized clinical reports of brain tumors from MRI volumes
Glioblasted is a machine learning model to detect glioblastoma, a high-grade, aggressive form of brain and/or spine cancer.
Code to preprocess, segment, and fuse glioma MRI scans based on the BraTS Toolkit manuscript.
Glioblastoma tumour classfication and tumour grade segmentattion using U-NET CNN
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.
Automating exosome-based glioblastoma diagnosis through bioinformatics and machine learning
A fast C++ based implementation of the CA-PPMx by Chandra et al. (2023+).
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.
Glioblastoma multiforme (GBM) biomarker knowledge base
Code used to create the core and extended GBmap, including downstream analyses (cell-cell interactions, spatial transcriptomics deconvolution) and how to produce the figures.
https://doi.org/10.5281/zenodo.6941367 - Spatiotemporal-Aware Glioblastoma Multiforme Tumor Growth Modeling with Deep Encoder-Decoder Networks
This repository contains primary source code for "Transcriptomic portraits and molecular pathway activation features of spinal intramedullary astrocytomas" manuscript.
This repository contains the code for my MSc project titled "Investigating therapy-driven changes in isoform expression in glioblastoma"
Source code for "Overexpression of CRNDE in glioblastoma is a poor survival prognosis biomarker" paper
Reference code for "Improved Prediction of Surgical Resectability in Patients with Glioblastoma using an Artificial Neural Network"
Image process framework to easily analyse fluorescent glioblastoma cells in pattern of neurons.
Methods for training and interpreting deep radiogenomic neural networks
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.
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