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 251–260 of 424 papers

Title	Date	Tasks	Status
Distributionally Robust Optimization and Invariant Representation Learning for Addressing Subgroup Underrepresentation: Mechanisms and Limitations	Aug 12, 2023	image-classificationImage Classification	—Unverified
Few-shot medical image classification with simple shape and texture text descriptors using vision-language models	Aug 8, 2023	Classificationimage-classification	—Unverified
Unlearning Spurious Correlations in Chest X-ray Classification	Aug 2, 2023	Classificationimage-classification	—Unverified
DINO-CXR: A self supervised method based on vision transformer for chest X-ray classification	Aug 1, 2023	COVID-19 DiagnosisImage Classification	—Unverified
L3DMC: Lifelong Learning using Distillation via Mixed-Curvature Space	Jul 31, 2023	image-classificationImage Classification	CodeCode Available
Class-Specific Distribution Alignment for Semi-Supervised Medical Image Classification	Jul 29, 2023	image-classificationImage Classification	—Unverified
Compact & Capable: Harnessing Graph Neural Networks and Edge Convolution for Medical Image Classification	Jul 24, 2023	Drug DiscoveryGraph Attention	—Unverified
Class Attention to Regions of Lesion for Imbalanced Medical Image Recognition	Jul 19, 2023	image-classificationImage Classification	—Unverified
Learning from Exemplary Explanations	Jul 12, 2023	Classificationimage-classification	—Unverified
The Whole Pathological Slide Classification via Weakly Supervised Learning	Jul 12, 2023	ClassificationContrastive Learning	—Unverified

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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