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 181–190 of 424 papers

Title	Date	Tasks	Status	Hype
Multiclass Alignment of Confidence and Certainty for Network Calibration	Sep 6, 2023	image-classificationImage Classification	—Unverified	0
Privacy-Preserving Medical Image Classification through Deep Learning and Matrix Decomposition	Aug 31, 2023	image-classificationImage Classification	—Unverified	0
Concept Bottleneck with Visual Concept Filtering for Explainable Medical Image Classification	Aug 23, 2023	image-classificationImage Classification	—Unverified	0
How Does Pruning Impact Long-Tailed Multi-Label Medical Image Classifiers?	Aug 17, 2023	image-classificationImage Classification	CodeCode Available	1
Enhancing Network Initialization for Medical AI Models Using Large-Scale, Unlabeled Natural Images	Aug 15, 2023	ClassificationDiagnostic	CodeCode Available	1
Robustness Stress Testing in Medical Image Classification	Aug 14, 2023	Classificationimage-classification	CodeCode Available	0
Distributionally Robust Optimization and Invariant Representation Learning for Addressing Subgroup Underrepresentation: Mechanisms and Limitations	Aug 12, 2023	image-classificationImage Classification	—Unverified	0
Fine-Grained Self-Supervised Learning with Jigsaw Puzzles for Medical Image Classification	Aug 10, 2023	image-classificationImage Classification	CodeCode Available	1
Robust Asymmetric Loss for Multi-Label Long-Tailed Learning	Aug 10, 2023	image-classificationImage Classification	CodeCode Available	1
Improving Medical Image Classification in Noisy Labels Using Only Self-supervised Pretraining	Aug 8, 2023	Classificationimage-classification	CodeCode Available	1

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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