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 161–170 of 424 papers

Title	Date	Tasks	Status	Hype
ARIA: On the Interaction Between Architectures, Initialization and Aggregation Methods for Federated Visual Classification	Nov 24, 2023	Federated Learningimage-classification	CodeCode Available	0
A Parameterized Generative Adversarial Network Using Cyclic Projection for Explainable Medical Image Classification	Nov 24, 2023	Data AugmentationGenerative Adversarial Network	CodeCode Available	0
Review of AlexNet for Medical Image Classification	Nov 15, 2023	Classificationimage-classification	—Unverified	0
Connecting the Dots: Graph Neural Network Powered Ensemble and Classification of Medical Images	Nov 13, 2023	BenchmarkingClassification	CodeCode Available	0
Medi-CAT: Contrastive Adversarial Training for Medical Image Classification	Oct 31, 2023	Contrastive Learningimage-classification	—Unverified	0
Are Natural Domain Foundation Models Useful for Medical Image Classification?	Oct 30, 2023	image-classificationImage Classification	CodeCode Available	1
GPT-4 Vision on Medical Image Classification -- A Case Study on COVID-19 Dataset	Oct 27, 2023	Diagnosticimage-classification	—Unverified	0
Interpretable Medical Image Classification using Prototype Learning and Privileged Information	Oct 24, 2023	image-classificationImage Classification	CodeCode Available	1
Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensembles	Oct 24, 2023	Data Augmentationimage-classification	—Unverified	0
Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge	Oct 24, 2023	ClassificationDiagnostic	—Unverified	0

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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