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 171–180 of 424 papers

Title	Date	Tasks	Status
Graph Neural Networks: A suitable Alternative to MLPs in Latent 3D Medical Image Classification?	Jul 24, 2024	Classificationgraph construction	CodeCode Available
Addressing Imbalance for Class Incremental Learning in Medical Image Classification	Jul 18, 2024	class-incremental learningClass Incremental Learning	—Unverified
Non-parametric regularization for class imbalance federated medical image classification	Jul 17, 2024	Federated Learningimage-classification	CodeCode Available
Probing the Efficacy of Federated Parameter-Efficient Fine-Tuning of Vision Transformers for Medical Image Classification	Jul 16, 2024	Federated Learningimage-classification	—Unverified
HDKD: Hybrid Data-Efficient Knowledge Distillation Network for Medical Image Classification	Jul 10, 2024	Computational Efficiencyimage-classification	CodeCode Available
Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label Noise	Jul 8, 2024	Active LearningClassification	CodeCode Available
Evaluating the Fairness of Neural Collapse in Medical Image Classification	Jul 8, 2024	Deep LearningFairness	—Unverified
Federated Distillation for Medical Image Classification: Towards Trustworthy Computer-Aided Diagnosis	Jul 2, 2024	ClassificationFederated Learning	—Unverified
Multi-branch CNN and grouping cascade attention for medical image classification	Jul 1, 2024	Classificationimage-classification	—Unverified
Automated Web-Based Malaria Detection System with Machine Learning and Deep Learning Techniques: A Comparative Analysis	Jun 27, 2024	Medical Image ClassificationTransfer Learning	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