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 101–150 of 424 papers

TitleStatusHype
TSynD: Targeted Synthetic Data Generation for Enhanced Medical Image Classification—0
Robustly Optimized Deep Feature Decoupling Network for Fatty Liver Diseases DetectionCode0
Diff3Dformer: Leveraging Slice Sequence Diffusion for Enhanced 3D CT Classification with Transformer Networks—0
DiffExplainer: Unveiling Black Box Models Via Counterfactual GenerationCode0
Certification for Differentially Private Prediction in Gradient-Based TrainingCode0
Unleashing the Potential of Open-set Noisy Samples Against Label Noise for Medical Image Classification—0
Boosting Medical Image Classification with Segmentation Foundation Model—0
Comparison of fine-tuning strategies for transfer learning in medical image classification—0
Evolution-aware VAriance (EVA) Coreset Selection for Medical Image Classification—0
Aligning Human Knowledge with Visual Concepts Towards Explainable Medical Image Classification—0
Compute-Efficient Medical Image Classification with Softmax-Free Transformers and Sequence Normalization—0
CONFINE: Conformal Prediction for Interpretable Neural Networks—0
GenMix: Combining Generative and Mixture Data Augmentation for Medical Image Classification—0
Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modelingCode1
Achieving Fairness Through Channel Pruning for Dermatological Disease DiagnosisCode1
MoVL:Exploring Fusion Strategies for the Domain-Adaptive Application of Pretrained Models in Medical Imaging Tasks—0
Pseudo-Prompt Generating in Pre-trained Vision-Language Models for Multi-Label Medical Image ClassificationCode1
A Lightweight Neural Architecture Search Model for Medical Image Classification—0
Meta-Transfer Derm-Diagnosis: Exploring Few-Shot Learning and Transfer Learning for Skin Disease Classification in Long-Tail Distribution—0
Rethinking model prototyping through the MedMNIST+ dataset collectionCode1
Brain Storm Optimization Based Swarm Learning for Diabetic Retinopathy Image Classification—0
A review of deep learning-based information fusion techniques for multimodal medical image classification—0
Distributed Federated Learning-Based Deep Learning Model for Privacy MRI Brain Tumor Detection—0
PM2: A New Prompting Multi-modal Model Paradigm for Few-shot Medical Image Classification—0
CopilotCAD: Empowering Radiologists with Report Completion Models and Quantitative Evidence from Medical Image Foundation Models—0
Contrastive-Based Deep Embeddings for Label Noise-Resilient Histopathology Image ClassificationCode0
PairAug: What Can Augmented Image-Text Pairs Do for Radiology?Code1
Non-negative Subspace Feature Representation for Few-shot Learning in Medical Imaging—0
Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification—0
Improved EATFormer: A Vision Transformer for Medical Image Classification—0
A Systematic Review of Generalization Research in Medical Image Classification—0
Multiple Teachers-Meticulous Student: A Domain Adaptive Meta-Knowledge Distillation Model for Medical Image ClassificationCode0
ConDiSR: Contrastive Disentanglement and Style Regularization for Single Domain GeneralizationCode0
Iterative Online Image Synthesis via Diffusion Model for Imbalanced Classification—0
In-context learning enables multimodal large language models to classify cancer pathology images—0
Fine-grained Prompt Tuning: A Parameter and Memory Efficient Transfer Learning Method for High-resolution Medical Image ClassificationCode1
Dynamic Perturbation-Adaptive Adversarial Training on Medical Image Classification—0
BSDA: Bayesian Random Semantic Data Augmentation for Medical Image ClassificationCode0
Debiased Noise Editing on Foundation Models for Fair Medical Image ClassificationCode0
MedMamba: Vision Mamba for Medical Image ClassificationCode4
Investigating the Robustness of Vision Transformers against Label Noise in Medical Image Classification—0
Integrating kNN with Foundation Models for Adaptable and Privacy-Aware Image ClassificationCode0
Only My Model On My Data: A Privacy Preserving Approach Protecting one Model and Deceiving Unauthorized Black-Box Models—0
Comparative Analysis of ImageNet Pre-Trained Deep Learning Models and DINOv2 in Medical Imaging ClassificationCode0
Exploring Low-Resource Medical Image Classification with Weakly Supervised Prompt Learning—0
SynthVision - Harnessing Minimal Input for Maximal Output in Computer Vision Models using Synthetic Image data—0
InceptionCapsule: Inception-Resnet and CapsuleNet with self-attention for medical image Classification—0
A Single Graph Convolution Is All You Need: Efficient Grayscale Image ClassificationCode1
Exploring the Transferability of a Foundation Model for Fundus Images: Application to Hypertensive Retinopathy—0
Medical Image Debiasing by Learning Adaptive Agreement from a Biased CouncilCode0
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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