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 371–380 of 424 papers

Title	Date	Tasks	Status
Aggregative Self-Supervised Feature Learning from a Limited Sample	Dec 14, 2020	image-classificationImage Classification	—Unverified
Application of the Neural Network Dependability Kit in Real-World Environments	Dec 14, 2020	ClassificationGeneral Classification	—Unverified
Combining Similarity and Adversarial Learning to Generate Visual Explanation: Application to Medical Image Classification	Dec 14, 2020	General Classificationimage-classification	—Unverified
Distant Domain Transfer Learning for Medical Imaging	Dec 10, 2020	ClassificationComputed Tomography (CT)	—Unverified
SAG-GAN: Semi-Supervised Attention-Guided GANs for Data Augmentation on Medical Images	Nov 15, 2020	ClassificationData Augmentation	—Unverified
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges	Nov 12, 2020	Decision MakingEnsemble Learning	—Unverified
Cross-Modal Information Maximization for Medical Imaging: CMIM	Oct 20, 2020	image-classificationImage Classification	—Unverified
Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization	Oct 9, 2020	Breast Cancer DetectionDecision Making	—Unverified
Emergent symbolic language based deep medical image classification	Aug 22, 2020	ClassificationDecision Making	CodeCode Available
An Aggregate Method for Thorax Diseases Classification	Aug 7, 2020	ClassificationGeneral 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