IRIS-MRS-AI is a tool that classifies IDH and TERTp mutations in gliomas. Besides these capabilities, IRIS-MRS-AI is a tool that can create custom models using users' data.

Exploring the Information Content of Glioma Differentiation using SDEs

CIS Research Program 2022; MIT Professor Manolis Kellis; Machine Learning and Deep Learing in Genomics and Health; U-Net CNN LGG Segmentation - concatenation hyperparameter tuning

Brain tumor classification using normal cnn . The code here is completely basic and shows how to get an accuracy also how to classify a certain data.

IDH Classification for Gliomas using CNN and Transformers.

This repo is for segmentation of T2 hyp regions in gliomas.

🤗 HuggingFace space for Raidionics 🤗

Nondestructive Spatial Lipidomics for Glioma Classification - Tissue Similarity and Grading

TensorFlow Version of AMF-Net for glioma grading and the classification of meningiomas and gliomas

Nondestructive Spatial Lipidomics for Glioma Classification - Tissue Similarity and Grading

Multimodal Context-Aware Detection of Glioma Biomarkers using MRI and WSI

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.

Calculation of 2D Fractal Dimension and Lacunarity in MR Images of gliomas

Pathomics Research Projects

🤗 neukit: web application for automatic brain extraction and preoperative tumor segmentation from MRI

Glioblastoma multiforme (GBM) biomarker knowledge base

An implementation of Mask R-CNN algorithm to perform automatic object detection, localization, classification and instance segmentation of immunoreactive tumor cells on Ki-67 stained glioma images.

We provide a method to extract the tractographic features from structural MR images for patients with brain tumor

NiftyNet-based implementation of Autofocus Net and Autofocus Layer.

In this project, we created a convolutional neural network using the EfficientNetB1 model in Keras to perform Image Classification of MRI brain scans with reasonably high (97.4%) accuracy.