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 261–270 of 424 papers

Title	Date	Tasks	Status	Hype
CEC-CNN: A Consecutive Expansion-Contraction Convolutional Network for Very Small Resolution Medical Image Classification	Sep 27, 2022	image-classificationImage Classification	—Unverified	0
HiFuse: Hierarchical Multi-Scale Feature Fusion Network for Medical Image Classification	Sep 21, 2022	Classificationimage-classification	CodeCode Available	2
Moving from 2D to 3D: volumetric medical image classification for rectal cancer staging	Sep 13, 2022	image-classificationImage Classification	CodeCode Available	0
Single-Stage Broad Multi-Instance Multi-Label Learning (BMIML) with Diverse Inter-Correlations and its application to medical image classification	Sep 6, 2022	image-classificationImage Classification	—Unverified	0
Consistency-Based Semi-supervised Evidential Active Learning for Diagnostic Radiograph Classification	Sep 5, 2022	Active LearningDiagnostic	—Unverified	0
ProCo: Prototype-aware Contrastive Learning for Long-tailed Medical Image Classification	Sep 1, 2022	ClassificationContrastive Learning	CodeCode Available	0
Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study	Aug 29, 2022	Classificationimage-classification	CodeCode Available	1
OpenMedIA: Open-Source Medical Image Analysis Toolbox and Benchmark under Heterogeneous AI Computing Platforms	Aug 11, 2022	image-classificationImage Classification	—Unverified	0
PatchDropout: Economizing Vision Transformers Using Patch Dropout	Aug 10, 2022	image-classificationImage Classification	CodeCode Available	1
RadTex: Learning Efficient Radiograph Representations from Text Reports	Aug 5, 2022	ClassificationDecoder	—Unverified	0

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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