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 81–90 of 424 papers

Title	Date	Tasks	Status	Hype
Rethinking Transfer Learning for Medical Image Classification	Jun 9, 2021	Classificationimage-classification	CodeCode Available	1
Mixture Outlier Exposure: Towards Out-of-Distribution Detection in Fine-grained Environments	Jun 7, 2021	image-classificationImage Classification	CodeCode Available	1
Meta-Learning with Fewer Tasks through Task Interpolation	Jun 4, 2021	image-classificationImage Classification	CodeCode Available	1
MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement Learning	Mar 26, 2021	Deep Reinforcement LearningGeneral Classification	CodeCode Available	1
Big Self-Supervised Models Advance Medical Image Classification	Jan 13, 2021	ClassificationContrastive Learning	CodeCode Available	1
Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification	Dec 6, 2020	ClassificationGeneral Classification	CodeCode Available	1
Synthesis of COVID-19 Chest X-rays using Unpaired Image-to-Image Translation	Oct 20, 2020	COVID-19 DiagnosisDomain Adaptation	CodeCode Available	1
Identifying Melanoma Images using EfficientNet Ensemble: Winning Solution to the SIIM-ISIC Melanoma Classification Challenge	Oct 11, 2020	Medical Image Classification	CodeCode Available	1
Contrastive Learning of Medical Visual Representations from Paired Images and Text	Oct 2, 2020	Contrastive LearningDescriptive	CodeCode Available	1
Semi-supervised Medical Image Classification with Global Latent Mixing	May 22, 2020	ClassificationGeneral 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