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 101–110 of 424 papers

Title	Date	Tasks	Status	Score
KPL: Training-Free Medical Knowledge Mining of Vision-Language Models	Jan 20, 2025	Classificationimage-classification	CodeCode Available	5
BayTTA: Uncertainty-aware medical image classification with optimized test-time augmentation using Bayesian model averaging	Jun 25, 2024	image-classificationImage Classification	CodeCode Available	5
L3DMC: Lifelong Learning using Distillation via Mixed-Curvature Space	Jul 31, 2023	image-classificationImage Classification	CodeCode Available	5
LMFLOSS: A Hybrid Loss For Imbalanced Medical Image Classification	Dec 24, 2022	Classificationimage-classification	CodeCode Available	5
Making the Most of Text Semantics to Improve Biomedical Vision--Language Processing	Apr 21, 2022	Contrastive LearningLanguage Modeling	CodeCode Available	5
BSDA: Bayesian Random Semantic Data Augmentation for Medical Image Classification	Mar 10, 2024	Data AugmentationDiversity	CodeCode Available	5
Counterfactual Explanation and Instance-Generation using Cycle-Consistent Generative Adversarial Networks	Jan 21, 2023	counterfactualCounterfactual Explanation	CodeCode Available	5
Bayesian Statistics Guided Label Refurbishment Mechanism: Mitigating Label Noise in Medical Image Classification	Jun 23, 2021	Classificationimage-classification	CodeCode Available	5
Automatic Acne Object Detection and Acne Severity Grading Using Smartphone Images and Artificial Intelligence	Aug 3, 2022	Acne Severity GradingMedical Image Classification	CodeCode Available	5
Improving the repeatability of deep learning models with Monte Carlo dropout	Feb 15, 2022	AttributeBinary Classification	CodeCode Available	5

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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