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 291–300 of 424 papers

Title	Date	Tasks	Status
Multiclass Alignment of Confidence and Certainty for Network Calibration	Sep 6, 2023	image-classificationImage Classification	—Unverified
Multi-Instance Learning by Utilizing Structural Relationship among Instances	Feb 3, 2021	Graph Attentionimage-classification	—Unverified
Multi-Instance Multi-Scale CNN for Medical Image Classification	Jul 4, 2019	ClassificationGeneral Classification	—Unverified
A systematic review of transfer learning based approaches for diabetic retinopathy detection	May 28, 2021	Diabetic Retinopathy Detectionimage-classification	—Unverified
4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection	Jun 26, 2020	General Classificationimage-classification	—Unverified
Multi-Sample ζ-mixup: Richer, More Realistic Synthetic Samples from a p-Series Interpolant	Apr 7, 2022	Data AugmentationDiversity	—Unverified
Mutual Attention-based Hybrid Dimensional Network for Multimodal Imaging Computer-aided Diagnosis	Jan 24, 2022	Diagnosticimage-classification	—Unverified
Non-negative Subspace Feature Representation for Few-shot Learning in Medical Imaging	Apr 3, 2024	AttributeDimensionality Reduction	—Unverified
Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge	Oct 24, 2023	ClassificationDiagnostic	—Unverified
Vision Transformers in Medical Imaging: A Review	Nov 18, 2022	DecoderDiversity	—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