SOTAVerified

Medical Image Classification

Medical Image Classification is a task in medical image analysis that involves classifying medical images, such as X-rays, MRI scans, and CT scans, into different categories based on the type of image or the presence of specific structures or diseases. The goal is to use computer algorithms to automatically identify and classify medical images based on their content, which can help in diagnosis, treatment planning, and disease monitoring.

Papers

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Showing 176–200 of 424 papers

TitleStatusHype
KPL: Training-Free Medical Knowledge Mining of Vision-Language ModelsCode0
DiffExplainer: Unveiling Black Box Models Via Counterfactual GenerationCode0
Can Score-Based Generative Modeling Effectively Handle Medical Image Classification?Code0
HDKD: Hybrid Data-Efficient Knowledge Distillation Network for Medical Image ClassificationCode0
ISLE: An Intelligent Streaming Framework for High-Throughput AI Inference in Medical ImagingCode0
Homogeneous Learning: Self-Attention Decentralized Deep LearningCode0
Graph Neural Networks: A suitable Alternative to MLPs in Latent 3D Medical Image Classification?Code0
Detection of Dementia Through 3D Convolutional Neural Networks Based on Amyloid PETCode0
Detecting Shortcuts in Medical Images -- A Case Study in Chest X-raysCode0
Generating customized prompts for Zero-Shot Rare Event Medical Image Classification using LLMCode0
L3DMC: Lifelong Learning using Distillation via Mixed-Curvature SpaceCode0
MIAFEx: An Attention-based Feature Extraction Method for Medical Image ClassificationCode0
Robustness Stress Testing in Medical Image ClassificationCode0
Boosting for Bounding the Worst-class Error—0
Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification—0
Deep reinforced active learning for multi-class image classification—0
Deeply Supervised Layer Selective Attention Network: Towards Label-Efficient Learning for Medical Image Classification—0
An ensemble framework approach of hybrid Quantum convolutional neural networks for classification of breast cancer images—0
Deep learning pipeline for image classification on mobile phones—0
Deep Learning in Medical Image Classification from MRI-based Brain Tumor Images—0
Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations—0
Deep Learning in Image Classification: Evaluating VGG19's Performance on Complex Visual Data—0
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning—0
Analysis of Explainable Artificial Intelligence Methods on Medical Image Classification—0
Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities—0
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All datasetsNCT-CRC-HE-100KIDRiDImageNetPCOS ClassificationCheXphotoGalaxy10 DECalsISIC 2017ISIC 2020 Challenge DatasetMalaria DatasetOASIS 3

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Efficientnet-b0Accuracy (%)95.59—Unverified
2ResNeXt-50-32x4dAccuracy (%)95.46—Unverified
3RegNetY-3.2GFAccuracy (%)95.42—Unverified
4ResNet-50Accuracy (%)94.72—Unverified
5DenseNet-169Accuracy (%)94.41—Unverified
6Res2Net-50Accuracy (%)93.37—Unverified
7ResNet-18Accuracy (%)92.66—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet-152Accuracy (% )86.56—Unverified
2Beta-RankAccuracy81.88—Unverified
#ModelMetricClaimedVerifiedStatus
1DaViT-SGFLOPs8.8—Unverified
2DaViT-TGFLOPs4.5—Unverified
#ModelMetricClaimedVerifiedStatus
1InceptionV31:1 Accuracy90.2—Unverified
2EfficientNet B71:1 Accuracy88.9—Unverified
#ModelMetricClaimedVerifiedStatus
1PTRNMean AUC0.85—Unverified
#ModelMetricClaimedVerifiedStatus
1AstroformerTop-1 Accuracy (%)94.87—Unverified
#ModelMetricClaimedVerifiedStatus
1Beta-RankAccuracy72.44—Unverified
#ModelMetricClaimedVerifiedStatus
1EfficientNet EnsembleAUC0.95—Unverified
#ModelMetricClaimedVerifiedStatus
1SNAPSHOT ENSEMBLEF1 score99.37—Unverified
#ModelMetricClaimedVerifiedStatus
13D CNNAUC87—Unverified