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Use of state of the art Convolutional neural network architectures including 3D UNet, 3D VNet and 2D UNets for Brain Tumor Segmentation and using segmented image features for Survival Prediction of patients through deep neural networks.
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.
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.
The official implementations of our BIBM'24 paper: Focus on Focus: Focus-oriented Representation Learning and Multi-view Cross-modal Alignment for Glioma Grading
NEXT 3D is a preclinical tool to optimize nanoparticle designs for BBB penetration in the treatment of glioblastoma multiforme, a proliferative central nervous system cancer.
Glioblasted is a machine learning model to assist in the detection of glioblastoma multiforme, a high-grade, aggressive form of central nervous system cancer.
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