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 351–360 of 424 papers

Title	Date	Tasks	Status
Robust Learning at Noisy Labeled Medical Images: Applied to Skin Lesion Classification	Jan 23, 2019	General Classificationimage-classification	—Unverified
Dual-View Pyramid Pooling in Deep Neural Networks for Improved Medical Image Classification and Confidence Calibration	Aug 6, 2024	Classificationimage-classification	—Unverified
Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training	May 10, 2022	image-classificationImage Classification	—Unverified
Unlearning Spurious Correlations in Chest X-ray Classification	Aug 2, 2023	Classificationimage-classification	—Unverified
Robust training of recurrent neural networks to handle missing data for disease progression modeling	Aug 16, 2018	image-classificationImage Classification	—Unverified
Robust Training with Data Augmentation for Medical Imaging Classification	Jun 20, 2025	Data AugmentationDiagnostic	—Unverified
RR-CP: Reliable-Region-Based Conformal Prediction for Trustworthy Medical Image Classification	Sep 9, 2023	Conformal PredictionDecision Making	—Unverified
SAG-GAN: Semi-Supervised Attention-Guided GANs for Data Augmentation on Medical Images	Nov 15, 2020	ClassificationData Augmentation	—Unverified
Sample selection with noise rate estimation in noise learning of medical image analysis	Dec 23, 2023	DiagnosticImage Classification	—Unverified
Deep reinforced active learning for multi-class image classification	Jun 20, 2022	Active LearningClassification	—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