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Glioblastoma and Survival Prediction
Glioblastoma is a stage IV highly invasive astrocytoma tumor. Its heterogeneous appearance in MRI poses a critical challenge in diagnosis, prognosis...
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Robustifying Automatic Assessment of Brain Tumor Progression from MRI
Accurate assessment of brain tumor progression from magnetic resonance imaging is a critical issue in clinical practice which allows us to precisely...
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Multimodal Context-Aware Detection of Glioma Biomarkers Using MRI and WSI
The most malignant tumors of the central nervous system are adult-type diffuse gliomas. Historically, glioma subtype classification has been based on...
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Simple and Fast Convolutional Neural Network Applied to Median Cross Sections for Predicting the Presence of MGMT Promoter Methylation in FLAIR MRI Scans
In this paper we present a small and fast Convolutional Neural Network (CNN) used to predict the presence of MGMT promoter methylation in Magnetic...
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Modified MobileNet for Patient Survival Prediction
Glioblastoma is a type of malignant tumor that varies significantly in size, shape, and location. The study of this type of tumor, one of which is...
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Exploiting microRNA Expression Data for the Diagnosis of Disease Conditions and the Discovery of Novel Biomarkers
MicroRNAs (miRNAs) play key roles in diseases and their detection in circulating biofluids makes them optimal candidate as disease biomarkers for...
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MS UNet: Multi-scale 3D UNet for Brain Tumor Segmentation
A deep convolutional neural network (CNN) achieves remarkable performance for medical image analysis. UNet is the primary source in the performance...
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Multi-path Feature Fusion and Channel Feature Pyramid for Brain Tumor Segmentation in MRI
Automated segmentation of gliomas in MRI images is crucial for timely diagnosis and treatment planning. In this paper, we propose an encoder-decoder...
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An automated and risk free WHO grading of glioma from MRI images using CNN
Glioma is among aggressive and common brain tumors, with a low survival rate, in its highest grade. Invasive methods, i.e., biopsy and spinal tap are...
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Automatic Classification of Brain Tumor Types with the MRI Scans and Histopathology Images
In the study, we used two neural networks, including VGG16 and Resnet50, to process the whole slide images with feature extracting. To classify the...
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Multimodal Brain Tumor Segmentation Using a 3D ResUNet in BraTS 2021
In this paper, we propose a multimodal brain tumor segmentation using a 3D ResUNet deep neural network architecture. Deep neural network has been...
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Temporal brain tumor progression tracking using deep learning and 3D MRI volume analysis
Cancer is among the most prevalent diseases globally. Concurrently, advances in artificial intelligence are revolutionizing brain tumor diagnosis by...
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Deep Learning Models for 3D MRI Brain Classification
This study evaluates the diagnostic performance for binary abnormality classification of deep learning models on various types of sequences from a...
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CA-Net: Collaborative Attention Network for Multi-modal Diagnosis of Gliomas
Deep neural network methods have led to impressive breakthroughs in the medical image field. Most of them focus on single-modal data, while diagnoses...
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Brain Tumor Segmentation in Multi-parametric Magnetic Resonance Imaging Using Model Ensembling and Super-resolution
Brain tumor segmentation in MRI offers critical quantitative imaging data to characterize and improve prognosis. The International Brain Tumor...
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Brain Tumor Classification with Multimodal MR and Pathology Images
Gliomas are the most common primary malignant tumors of the brain caused by glial cell canceration of the brain and spinal cord. Its incidence...
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Machine Learning for Time-to-Event Prediction and Survival Clustering: A Review from Statistics to Deep Neural Networks
Survival analysis is a statistical method used in computational biology to investigate the time until the occurrence of an event of interest, such as...
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Multi-channel Deep Transfer Learning for Nuclei Segmentation in Glioblastoma Cell Tissue Images
Segmentation and quantification of cell nuclei is an important task in tissue microscopy image analysis. We introduce a deep learning method...
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Quality-Aware Model Ensemble for Brain Tumor Segmentation
Automatic segmentation of brain tumors is still a challenging task. To improve the segmentation performance and better ensemble all the candidate...
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FedPIDAvg: A PID Controller Inspired Aggregation Method for Federated Learning
This paper presents FedPIDAvg, the winning submission to the Federated Tumor Segmentation Challenge 2022 (FETS22). Inspired by FedCostWAvg, our...