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
Exploring the Versatility of Zero-Shot CLIP for Interstitial Lung Disease Classification	Jun 1, 2023	ClassificationDiagnostic	—Unverified
Evaluating the Fairness of Neural Collapse in Medical Image Classification	Jul 8, 2024	Deep LearningFairness	—Unverified
Chest X-rays Classification: A Multi-Label and Fine-Grained Problem	Jul 19, 2018	General Classificationimage-classification	—Unverified
A ChatGPT Aided Explainable Framework for Zero-Shot Medical Image Diagnosis	Jul 5, 2023	Diagnosticimage-classification	—Unverified
Freeze the backbones: A Parameter-Efficient Contrastive Approach to Robust Medical Vision-Language Pre-training	Jan 2, 2024	image-classificationImage Classification	—Unverified
FairREAD: Re-fusing Demographic Attributes after Disentanglement for Fair Medical Image Classification	Dec 20, 2024	DiagnosticDisentanglement	—Unverified
GenMix: Combining Generative and Mixture Data Augmentation for Medical Image Classification	May 31, 2024	Data AugmentationDiversity	—Unverified
Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification	Mar 2, 2023	ClusteringDomain Adaptation	—Unverified
Enhancing Transfer Learning for Medical Image Classification with SMOTE: A Comparative Study	Dec 28, 2024	Brain Tumor ClassificationDiabetic Retinopathy Detection	—Unverified
Application of the Neural Network Dependability Kit in Real-World Environments	Dec 14, 2020	ClassificationGeneral 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