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 91–100 of 424 papers

Title	Date	Tasks	Status	Hype
Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive Learning	Oct 17, 2022	Contrastive Learningimage-classification	CodeCode Available	1
Only Positive Cases: 5-fold High-order Attention Interaction Model for Skin Segmentation Derived Classification	Nov 27, 2023	Image SegmentationLesion Classification	CodeCode Available	1
Astroformer: More Data Might not be all you need for Classification	Apr 3, 2023	AllAstronomy	CodeCode Available	1
PatchDropout: Economizing Vision Transformers Using Patch Dropout	Aug 10, 2022	image-classificationImage Classification	CodeCode Available	1
A survey on attention mechanisms for medical applications: are we moving towards better algorithms?	Apr 26, 2022	image-classificationImage Classification	CodeCode Available	1
Deep Multimodal Guidance for Medical Image Classification	Mar 10, 2022	Brain Tumor ClassificationClassification	CodeCode Available	1
Boosting for Bounding the Worst-class Error	Oct 20, 2023	image-classificationImage Classification	—Unverified	0
Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification	Mar 26, 2024	Few-Shot Learningimage-classification	—Unverified	0
An ensemble framework approach of hybrid Quantum convolutional neural networks for classification of breast cancer images	Sep 24, 2024	image-classificationImage Classification	—Unverified	0
4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection	Jun 26, 2020	General Classificationimage-classification	—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