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 301–310 of 424 papers

Title	Date	Tasks	Status
SPLAL: Similarity-based pseudo-labeling with alignment loss for semi-supervised medical image classification	Jul 10, 2023	Classificationimage-classification	—Unverified
Splitfed learning without client-side synchronization: Analyzing client-side split network portion size to overall performance	Sep 19, 2021	Federated Learningimage-classification	—Unverified
Statistical Dependency Guided Contrastive Learning for Multiple Labeling in Prenatal Ultrasound	Aug 11, 2021	Contrastive LearningDiagnostic	—Unverified
SynthVision - Harnessing Minimal Input for Maximal Output in Computer Vision Models using Synthetic Image data	Feb 5, 2024	Experimental Designimage-classification	—Unverified
Text-guided Foundation Model Adaptation for Long-Tailed Medical Image Classification	Aug 27, 2024	DiagnosticGPU	—Unverified
Texture features in medical image analysis: a survey	Aug 2, 2022	image-classificationImage Classification	—Unverified
The GraphNet Zoo: An All-in-One Graph Based Deep Semi-Supervised Framework for Medical Image Classification	Mar 13, 2020	AllClassification	—Unverified
The Importance of Background Information for Out of Distribution Generalization	Jun 17, 2022	Domain Generalizationimage-classification	—Unverified
The Whole Pathological Slide Classification via Weakly Supervised Learning	Jul 12, 2023	ClassificationContrastive Learning	—Unverified
TOP-GAN: Label-Free Cancer Cell Classification Using Deep Learning with a Small Training Set	Dec 17, 2018	General ClassificationGenerative Adversarial Network	—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