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

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Showing 401–410 of 424 papers

Title	Date	Tasks	Status
Unsupervised Feature Learning with K-means and An Ensemble of Deep Convolutional Neural Networks for Medical Image Classification	Jun 7, 2019	ClusteringDeep Learning	—Unverified
Transfer Learning based Detection of Diabetic Retinopathy from Small Dataset	May 17, 2019	ClassificationDeep Learning	CodeCode Available
TRk-CNN: Transferable Ranking-CNN for image classification of glaucoma, glaucoma suspect, and normal eyes	May 16, 2019	ClassificationGeneral Classification	—Unverified
On evaluating CNN representations for low resource medical image classification	Mar 26, 2019	ClassificationGeneral Classification	—Unverified
Improving Sample Complexity with Observational Supervision	Mar 25, 2019	image-classificationImage Classification	—Unverified
Unsupervised Deep Transfer Feature Learning for Medical Image Classification	Mar 15, 2019	ClassificationGeneral Classification	—Unverified
Robust Learning at Noisy Labeled Medical Images: Applied to Skin Lesion Classification	Jan 23, 2019	General Classificationimage-classification	—Unverified
TOP-GAN: Label-Free Cancer Cell Classification Using Deep Learning with a Small Training Set	Dec 17, 2018	General ClassificationGenerative Adversarial Network	—Unverified
Effective, Fast, and Memory-Efficient Compressed Multi-function Convolutional Neural Networks for More Accurate Medical Image Classification	Nov 29, 2018	General Classificationimage-classification	—Unverified
Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images	Aug 22, 2018	Computational EfficiencyGeneral 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