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 321–330 of 424 papers

Title	Date	Tasks	Status
Unleashing the Potential of Open-set Noisy Samples Against Label Noise for Medical Image Classification	Jun 18, 2024	Classificationimage-classification	—Unverified
Unsupervised Deep Transfer Feature Learning for Medical Image Classification	Mar 15, 2019	ClassificationGeneral Classification	—Unverified
Unsupervised Domain Adaptation Using Feature Disentanglement And GCNs For Medical Image Classification	Jun 27, 2022	DisentanglementDomain Adaptation	—Unverified
Unsupervised Feature Learning with K-means and An Ensemble of Deep Convolutional Neural Networks for Medical Image Classification	Jun 7, 2019	ClusteringDeep Learning	—Unverified
Variational Knowledge Distillation for Disease Classification in Chest X-Rays	Mar 19, 2021	ClassificationGeneral Classification	—Unverified
Vision Through the Veil: Differential Privacy in Federated Learning for Medical Image Classification	Jun 30, 2023	Federated Learningimage-classification	—Unverified
Vision Transformers in Medical Imaging: A Review	Nov 18, 2022	DecoderDiversity	—Unverified
Visualization approach to assess the robustness of neural networks for medical image classification	Nov 19, 2019	General Classificationimage-classification	—Unverified
Visual Prompt Engineering for Medical Vision Language Models in Radiology	Aug 28, 2024	Classificationimage-classification	—Unverified
Weakly-supervised Generative Adversarial Networks for medical image classification	Nov 29, 2021	ClassificationContrastive Learning	—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