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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 121–130 of 424 papers

Title	Date	Tasks	Status
Embedding Task Knowledge into 3D Neural Networks via Self-supervised Learning	Jun 10, 2020	ClusteringGeneral Classification	—Unverified
Enhancing Image Classification in Small and Unbalanced Datasets through Synthetic Data Augmentation	Sep 16, 2024	Data AugmentationDiagnostic	—Unverified
CopilotCAD: Empowering Radiologists with Report Completion Models and Quantitative Evidence from Medical Image Foundation Models	Apr 11, 2024	Diagnosticimage-classification	—Unverified
Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification	Mar 26, 2024	Few-Shot Learningimage-classification	—Unverified
Convolutional XGBoost (C-XGBOOST) Model for Brain Tumor Detection	Jan 5, 2023	image-classificationImage Classification	—Unverified
Boosting for Bounding the Worst-class Error	Oct 20, 2023	image-classificationImage Classification	—Unverified
A Comprehensive Study of Modern Architectures and Regularization Approaches on CheXpert5000	Feb 13, 2023	image-classificationImage Classification	—Unverified
Contrastive Centroid Supervision Alleviates Domain Shift in Medical Image Classification	May 31, 2022	ClassificationDomain Adaptation	—Unverified
Embeddings are all you need! Achieving High Performance Medical Image Classification through Training-Free Embedding Analysis	Dec 12, 2024	AllClassification	—Unverified
A systematic review of transfer learning based approaches for diabetic retinopathy detection	May 28, 2021	Diabetic Retinopathy Detectionimage-classification	—Unverified

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All datasets NCT-CRC-HE-100K IDRiD ImageNet PCOS Classification CheXphoto Galaxy10 DECals ISIC 2017 ISIC 2020 Challenge Dataset Malaria Dataset OASIS 3

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Efficientnet-b0	Accuracy (%)	95.59	—	Unverified
2	ResNeXt-50-32x4d	Accuracy (%)	95.46	—	Unverified
3	RegNetY-3.2GF	Accuracy (%)	95.42	—	Unverified
4	ResNet-50	Accuracy (%)	94.72	—	Unverified
5	DenseNet-169	Accuracy (%)	94.41	—	Unverified
6	Res2Net-50	Accuracy (%)	93.37	—	Unverified
7	ResNet-18	Accuracy (%)	92.66	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ResNet-152	Accuracy (% )	86.56	—	Unverified
2	Beta-Rank	Accuracy	81.88	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DaViT-S	GFLOPs	8.8	—	Unverified
2	DaViT-T	GFLOPs	4.5	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	InceptionV3	1:1 Accuracy	90.2	—	Unverified
2	EfficientNet B7	1:1 Accuracy	88.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PTRN	Mean AUC	0.85	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Astroformer	Top-1 Accuracy (%)	94.87	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Beta-Rank	Accuracy	72.44	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	EfficientNet Ensemble	AUC	0.95	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SNAPSHOT ENSEMBLE	F1 score	99.37	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	3D CNN	AUC	87	—	Unverified