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	Hype
Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training	May 10, 2022	image-classificationImage Classification	—Unverified	0
Explainable Deep Learning Methods in Medical Image Classification: A Survey	May 10, 2022	Deep LearningExplainable artificial intelligence	CodeCode Available	1
Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification	May 8, 2022	Diagnosticimage-classification	CodeCode Available	0
A survey on attention mechanisms for medical applications: are we moving towards better algorithms?	Apr 26, 2022	image-classificationImage Classification	CodeCode Available	1
Making the Most of Text Semantics to Improve Biomedical Vision--Language Processing	Apr 21, 2022	Contrastive LearningLanguage Modeling	CodeCode Available	0
Multi-Sample ζ-mixup: Richer, More Realistic Synthetic Samples from a p-Series Interpolant	Apr 7, 2022	Data AugmentationDiversity	—Unverified	0
DaViT: Dual Attention Vision Transformers	Apr 7, 2022	Computational EfficiencyImage Classification	CodeCode Available	2
CAIPI in Practice: Towards Explainable Interactive Medical Image Classification	Apr 6, 2022	BIG-bench Machine LearningClassification	—Unverified	0
Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classification	Apr 4, 2022	AttributeClassification	—Unverified	0
Mix-up Self-Supervised Learning for Contrast-agnostic Applications	Apr 2, 2022	image-classificationImage Classification	—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