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 31–40 of 424 papers

Title	Date	Tasks	Status	Hype
A Single Graph Convolution Is All You Need: Efficient Grayscale Image Classification	Feb 1, 2024	AllClassification	CodeCode Available	1
Prompt-driven Latent Domain Generalization for Medical Image Classification	Jan 5, 2024	Domain Generalizationimage-classification	CodeCode Available	1
Building Universal Foundation Models for Medical Image Analysis with Spatially Adaptive Networks	Dec 12, 2023	image-classificationImage Classification	CodeCode Available	1
PHG-Net: Persistent Homology Guided Medical Image Classification	Nov 28, 2023	Classificationimage-classification	CodeCode Available	1
Only Positive Cases: 5-fold High-order Attention Interaction Model for Skin Segmentation Derived Classification	Nov 27, 2023	Image SegmentationLesion Classification	CodeCode Available	1
Are Natural Domain Foundation Models Useful for Medical Image Classification?	Oct 30, 2023	image-classificationImage Classification	CodeCode Available	1
Interpretable Medical Image Classification using Prototype Learning and Privileged Information	Oct 24, 2023	image-classificationImage Classification	CodeCode Available	1
fastMONAI: A low-code deep learning library for medical image analysis	Sep 26, 2023	Medical Image AnalysisMedical Image Classification	CodeCode Available	1
How Does Pruning Impact Long-Tailed Multi-Label Medical Image Classifiers?	Aug 17, 2023	image-classificationImage Classification	CodeCode Available	1
Enhancing Network Initialization for Medical AI Models Using Large-Scale, Unlabeled Natural Images	Aug 15, 2023	ClassificationDiagnostic	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