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 331–340 of 424 papers

Title	Date	Tasks	Status
Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classification	Apr 4, 2022	AttributeClassification	—Unverified
Mix-up Self-Supervised Learning for Contrast-agnostic Applications	Apr 2, 2022	image-classificationImage Classification	—Unverified
A Two-Stage Federated Transfer Learning Framework in Medical Images Classification on Limited Data: A COVID-19 Case Study	Mar 24, 2022	COVID-19 DiagnosisFederated Learning	—Unverified
Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward	Mar 21, 2022	image-classificationImage Classification	—Unverified
Meta Ordinal Regression Forest for Medical Image Classification with Ordinal Labels	Mar 15, 2022	Classificationfeature selection	—Unverified
FairPrune: Achieving Fairness Through Pruning for Dermatological Disease Diagnosis	Mar 4, 2022	Fairnessimage-classification	—Unverified
Improving the repeatability of deep learning models with Monte Carlo dropout	Feb 15, 2022	AttributeBinary Classification	CodeCode Available
Mutual Attention-based Hybrid Dimensional Network for Multimodal Imaging Computer-aided Diagnosis	Jan 24, 2022	Diagnosticimage-classification	—Unverified
Detection of Dementia Through 3D Convolutional Neural Networks Based on Amyloid PET	Jan 24, 2022	Medical Image Classification	CodeCode Available
Weakly-supervised Generative Adversarial Networks for medical image classification	Nov 29, 2021	ClassificationContrastive Learning	—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