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 351–360 of 424 papers

Title	Date	Tasks	Status
ISLE: An Intelligent Streaming Framework for High-Throughput AI Inference in Medical Imaging	May 24, 2023	Decision MakingImage Classification	CodeCode Available
HDKD: Hybrid Data-Efficient Knowledge Distillation Network for Medical Image Classification	Jul 10, 2024	Computational Efficiencyimage-classification	CodeCode Available
Connecting the Dots: Graph Neural Network Powered Ensemble and Classification of Medical Images	Nov 13, 2023	BenchmarkingClassification	CodeCode Available
ConDiSR: Contrastive Disentanglement and Style Regularization for Single Domain Generalization	Mar 14, 2024	ClassificationDisentanglement	CodeCode Available
Comparative Analysis of ImageNet Pre-Trained Deep Learning Models and DINOv2 in Medical Imaging Classification	Feb 12, 2024	image-classificationImage Classification	CodeCode Available
Graph Neural Networks: A suitable Alternative to MLPs in Latent 3D Medical Image Classification?	Jul 24, 2024	Classificationgraph construction	CodeCode Available
Medical Image Debiasing by Learning Adaptive Agreement from a Biased Council	Jan 22, 2024	Attributeimage-classification	CodeCode Available
Chest X-Ray Images Classification with CNN	Dec 31, 2022	ClassificationDiagnostic	CodeCode Available
Medical supervised masked autoencoders: Crafting a better masking strategy and efficient fine-tuning schedule for medical image classification	May 10, 2023	Computational Efficiencyimage-classification	CodeCode Available
Synthetic Medical Images from Dual Generative Adversarial Networks	Sep 6, 2017	image-classificationImage Classification	CodeCode Available

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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