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 271–280 of 424 papers

Title	Date	Tasks	Status
Exploring the Versatility of Zero-Shot CLIP for Interstitial Lung Disease Classification	Jun 1, 2023	ClassificationDiagnostic	—Unverified
ISLE: An Intelligent Streaming Framework for High-Throughput AI Inference in Medical Imaging	May 24, 2023	Decision MakingImage Classification	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
Forward-Forward Contrastive Learning	May 4, 2023	ClassificationContrastive Learning	—Unverified
FCA: Taming Long-tailed Federated Medical Image Classification by Classifier Anchoring	May 1, 2023	Federated Learningimage-classification	CodeCode Available
Beta-Rank: A Robust Convolutional Filter Pruning Method For Imbalanced Medical Image Analysis	Apr 15, 2023	Image ClassificationMedical Image Analysis	CodeCode Available
Scale Federated Learning for Label Set Mismatch in Medical Image Classification	Apr 14, 2023	Federated Learningimage-classification	CodeCode Available
Self-supervision for medical image classification: state-of-the-art performance with ~100 labeled training samples per class	Apr 11, 2023	image-classificationImage Classification	CodeCode Available
Universal Semi-Supervised Learning for Medical Image Classification	Apr 8, 2023	ClassificationDomain Adaptation	CodeCode Available
A Test Statistic Estimation-based Approach for Establishing Self-interpretable CNN-based Binary Classifiers	Mar 13, 2023	Binary ClassificationDecoder	—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