Papers Brain Tumor Classification
“Brain Tumor Classification” 태그가 달린 논문 92편 · 필터 해제
A Vision-Language Foundation Model for Precise and Comprehensive Brain Tumor Diagnosis from Preoperative Multimodal Data
Background Non-invasive presurgical diagnosis of brain tumor types from Magnetic Resonance Imaging (MRI) is essential but challenging due to overlapping imaging features across tumor types, inter-observer variability, an…
Brain Tumor ClassificationBrainFusionNet: a deep learning and XAI model to understand local, global, and sequential features of MRI images for improved brain tumour detection
The noise of Magnetic Resonance Imaging MRI poses challenges for Deep Learning DL when tumor boundaries are obscured tumor location and appearance are complex Therefore we develop BrainFusionNet that combines Convolution…
Brain Tumor ClassificationImage ClassificationTransfer LearningMulti-Class Brain Tumor Classification Using Advanced Deep Learning Models: A Comparative Study
Despite recent advancements in deep learning, accurately classifying brain tumors from MRI images continues to pose challenges. In this research, we present a comprehensive evaluation of five different convolutional neur…
Brain Tumor ClassificationBridging Topology and Deep Representation Learning: A TDA-ViT Fusion Model for Four-Class Brain Tumor Classification
Accurate brain tumor classification from magnetic resonance imaging (MRI) is a key requirement for early diagnosis and clinical decision-making. Vision Transformers (ViTs) have shown strong performance in medical image a…
Brain Tumor ClassificationRepresentation LearningQuantized Machine Learning Models for Medical Imaging in Low-Resource Healthcare Settings
Deep learning models have shown strong performance in medical image analysis, but deploying them in low-resource clinical environments remains difficult due to computational, memory, and power constraints. This paper pre…
Brain Tumor ClassificationKnowledge DistillationTransfer LearningBrain Tumor Classification in MRI Images: A Computationally Efficient Convolutional Neural Network
Improving patient outcomes depends on the prompt and accurate diagnosis of brain tumors, but manual MRI scan analysis is still time-consuming and unreliable. Although deep learning has shown promise, many of the models t…
Brain Tumor ClassificationBridging visual saliency and large language models for explainable deep learning in medical imaging
The opaque nature of deep learning models remains a significant barrier to their clinical adoption in medical imaging. This paper presents a multimodal explainability framework that bridges the gap between convolutional …
Brain Tumor ClassificationOrientation-Aware Unsupervised Domain Adaptation for Brain Tumor Classification Across Multi-Modal MRI
The clinical integration of deep learning models for brain tumor diagnosis in neuro-oncology is severely constrained by limited expert-annotated MRI data and substantial inter-institutional domain shift arising from vari…
Unsupervised Domain AdaptationBrain Tumor ClassificationTumorXAI: Self-Supervised Deep Learning Framework for Explainable Brain MRI Tumor Classification
Classifying brain tumors using magnetic resonance imaging (MRI) is crucial for early diagnosis and treatment; however, tumor heterogeneity and a dearth of annotated datasets restrict the use of supervised deep learning a…
Brain Tumor ClassificationSelf-Supervised Learningan interpretable vision transformer framework for automated brain tumor classification
Brain tumors represent one of the most critical neurological conditions, where early and accurate diagnosis is directly correlated with patient survival rates. Manual interpretation of Magnetic Resonance Imaging (MRI) sc…
Brain Tumor ClassificationClinically-Informed Modeling for Pediatric Brain Tumor Classification from Whole-Slide Histopathology Images
Accurate diagnosis of pediatric brain tumors, starting with histopathology, presents unique challenges for deep learning, including severe data scarcity, class imbalance, and fine-grained morphologic overlap across diagn…
Multiple Instance LearningBrain Tumor ClassificationRepresentation LearningContrastive LearningChaos-Enhanced Prototypical Networks for Few-Shot Medical Image Classification
The scarcity of labeled clinical data in oncology makes Few-Shot Learning (FSL) a critical framework for Computer Aided Diagnostics, but we observed that standard Prototypical Networks often struggle with the "prototype …
Medical Image ClassificationBrain Tumor ClassificationFew-Shot LearningDiffusion-Based Feature Denoising with NNMF for Robust handwritten digit multi-class classification
This work presents a robust multi-class classification framework for handwritten digits that combines diffusion-driven feature denoising with a hybrid feature representation. Inspired by our previous work on brain tumor …
Brain Tumor ClassificationMulti-class ClassificationOptimized Weighted Voting System for Brain Tumor Classification Using MRI Images
The accurate classification of brain tumors from MRI scans is essential for effective diagnosis and treatment planning. This paper presents a weighted ensemble learning approach that combines deep learning and traditiona…
Brain Tumor ClassificationEnsemble LearningEdge DetectionPhyDCM: A Reproducible Open-Source Framework for AI-Assisted Brain Tumor Classification from Multi-Sequence MRI
MRI-based medical imaging has become indispensable in modern clinical diagnosis, particularly for brain tumor detection. However, the rapid growth in data volume poses challenges for conventional diagnostic approaches. A…
Brain Tumor ClassificationData AugmentationEnhancing Brain Tumor Classification Using Vision Transformers with Colormap-Based Feature Representation on BRISC2025 Dataset
Accurate classification of brain tumors from magnetic resonance imaging (MRI) plays a critical role in early diagnosis and effective treatment planning. In this study, we propose a deep learning framework based on Vision…
Brain Tumor ClassificationBrain Tumor Classification from 3D MRI Using Persistent Homology and Betti Features: A Topological Data Analysis Approach on BraTS2020
Accurate and interpretable brain tumor classification from medical imaging remains a challenging problem due to the high dimensionality and complex structural patterns present in magnetic resonance imaging (MRI). In this…
Brain Tumor ClassificationBinary ClassificationDiffusion-Based Feature Denoising and Using NNMF for Robust Brain Tumor Classification
Brain tumor classification from magnetic resonance imaging, which is also known as MRI, plays a sensitive role in computer-assisted diagnosis systems. In recent years, deep learning models have achieved high classificati…
Medical Image ClassificationBrain Tumor ClassificationAdversarial RobustnessPathMoE: Interpretable Multimodal Interaction Experts for Pediatric Brain Tumor Classification
Accurate classification of pediatric central nervous system tumors remains challenging due to histological complexity and limited training data. While pathology foundation models have advanced whole-slide image (WSI) ana…
Brain Tumor ClassificationVariViT: A Vision Transformer for Variable Image Sizes
Vision Transformers (ViTs) have emerged as the state-of-the-art architecture in representation learning, leveraging self-attention mechanisms to excel in various tasks. ViTs split images into fixed-size patches, constrai…
Brain Tumor ClassificationRepresentation Learning