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
Deep reinforced active learning for multi-class image classification	Jun 20, 2022	Active LearningClassification	—Unverified
The Importance of Background Information for Out of Distribution Generalization	Jun 17, 2022	Domain Generalizationimage-classification	—Unverified
Video Capsule Endoscopy Classification using Focal Modulation Guided Convolutional Neural Network	Jun 16, 2022	Inductive LearningMedical Image Classification	CodeCode Available
Deep learning pipeline for image classification on mobile phones	May 31, 2022	ClassificationDeep Learning	—Unverified
Contrastive Centroid Supervision Alleviates Domain Shift in Medical Image Classification	May 31, 2022	ClassificationDomain Adaptation	—Unverified
Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training	May 10, 2022	image-classificationImage Classification	—Unverified
Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification	May 8, 2022	Diagnosticimage-classification	CodeCode Available
Making the Most of Text Semantics to Improve Biomedical Vision--Language Processing	Apr 21, 2022	Contrastive LearningLanguage Modeling	CodeCode Available
Multi-Sample ζ-mixup: Richer, More Realistic Synthetic Samples from a p-Series Interpolant	Apr 7, 2022	Data AugmentationDiversity	—Unverified
CAIPI in Practice: Towards Explainable Interactive Medical Image Classification	Apr 6, 2022	BIG-bench Machine 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