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 321–330 of 424 papers

Title	Date	Tasks	Status
TRk-CNN: Transferable Ranking-CNN for image classification of glaucoma, glaucoma suspect, and normal eyes	May 16, 2019	ClassificationGeneral Classification	—Unverified
TSynD: Targeted Synthetic Data Generation for Enhanced Medical Image Classification	Jun 25, 2024	image-classificationImage Classification	—Unverified
Application of the Neural Network Dependability Kit in Real-World Environments	Dec 14, 2020	ClassificationGeneral Classification	—Unverified
A Novel Momentum-Based Deep Learning Techniques for Medical Image Classification and Segmentation	Aug 11, 2024	Benchmarkingimage-classification	—Unverified
Uncertainties of Latent Representations in Computer Vision	Aug 26, 2024	image-classificationImage Classification	—Unverified
Privacy-Preserving Constrained Domain Generalization via Gradient Alignment	May 14, 2021	ClassificationDomain Generalization	—Unverified
Privacy-preserving Machine Learning for Medical Image Classification	Aug 29, 2021	BIG-bench Machine LearningClassification	—Unverified
Privacy-Preserving Medical Image Classification through Deep Learning and Matrix Decomposition	Aug 31, 2023	image-classificationImage Classification	—Unverified
4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection	Jun 26, 2020	General Classificationimage-classification	—Unverified
Probing the Efficacy of Federated Parameter-Efficient Fine-Tuning of Vision Transformers for Medical Image Classification	Jul 16, 2024	Federated Learningimage-classification	—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