Brain Tumor Classification
10 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Brain Tumor Classification
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Brain Tumor MRI Dataset
Most implemented papers
HMM Model for Brain Tumor Detection and Classification
Brain tumor is one of the significant problems that has taken the life of a lot of people in recent times.
Deep Multimodal Guidance for Medical Image Classification
Furthermore, in the case of brain tumor classification, our method outperforms the model trained on the superior modality while producing comparable results to the model that uses both modalities during inference.
MProtoNet: A Case-Based Interpretable Model for Brain Tumor Classification with 3D Multi-parametric Magnetic Resonance Imaging
aywi/mprotonet
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Recent applications of deep convolutional neural networks in medical imaging raise concerns about their interpretability.
LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching
duyhominhnguyen/LVM-Med
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NeurIPS 2023
While pre-trained deep networks on ImageNet and vision-language foundation models trained on web-scale data are prevailing approaches, their effectiveness on medical tasks is limited due to the significant domain shift between natural and medical images.
Feature Imitating Networks Enhance The Performance, Reliability And Speed Of Deep Learning On Biomedical Image Processing Tasks
haail/fins-for-biomedical-imaging
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Feature-Imitating-Networks (FINs) are neural networks that are first trained to approximate closed-form statistical features (e. g. Entropy), and then embedded into other networks to enhance their performance.
Optimizing Brain Tumor Classification: A Comprehensive Study on Transfer Learning and Imbalance Handling in Deep Learning Models
By leveraging a publicly available Brain MRI dataset, the experiment evaluated various transfer learning models for classifying different tumor types, including meningioma, glioma, and pituitary tumors.
MAProtoNet: A Multi-scale Attentive Interpretable Prototypical Part Network for 3D Magnetic Resonance Imaging Brain Tumor Classification
Specifically, we introduce a concise multi-scale module to merge attentive features from quadruplet attention layers, and produces attribution maps.
FedBrain-Distill: Communication-Efficient Federated Brain Tumor Classification Using Ensemble Knowledge Distillation on Non-IID Data
russelljeffrey/FedBrain-Distill
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In this paper, we propose FedBrain-Distill, an approach that leverages Knowledge Distillation (KD) in an FL setting that maintains the users privacy and ensures the independence of FL clients in terms of model architecture.
Enhanced MRI brain tumor detection and classification via topological data analysis and low-rank tensor decomposition
The promising results show that the integration of TDA, ML, and low-rank approximation methods is a successful approach for brain tumor identification and categorization, providing a solid foundation for further study and clinical application.
Edge-Enhanced Dilated Residual Attention Network for Multimodal Medical Image Fusion
Multimodal medical image fusion is a crucial task that combines complementary information from different imaging modalities into a unified representation, thereby enhancing diagnostic accuracy and treatment planning.
