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 171–180 of 424 papers

Title	Date	Tasks	Status	Hype
Boosting for Bounding the Worst-class Error	Oct 20, 2023	image-classificationImage Classification	—Unverified	0
Plug-and-Play Feature Generation for Few-Shot Medical Image Classification	Oct 14, 2023	Few-Shot Learningimage-classification	—Unverified	0
Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models	Oct 4, 2023	Diagnosticimage-classification	—Unverified	0
Mind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI Models	Oct 1, 2023	DiagnosticDiversity	CodeCode Available	0
Reconstruction of Patient-Specific Confounders in AI-based Radiologic Image Interpretation using Generative Pretraining	Sep 29, 2023	Diagnosticimage-classification	CodeCode Available	0
fastMONAI: A low-code deep learning library for medical image analysis	Sep 26, 2023	Medical Image AnalysisMedical Image Classification	CodeCode Available	1
Applications of Sequential Learning for Medical Image Classification	Sep 26, 2023	Classificationimage-classification	—Unverified	0
Understanding Calibration of Deep Neural Networks for Medical Image Classification	Sep 22, 2023	image-classificationImage Classification	—Unverified	0
Causality-Driven One-Shot Learning for Prostate Cancer Grading from MRI	Sep 19, 2023	Decision Makingimage-classification	—Unverified	0
RR-CP: Reliable-Region-Based Conformal Prediction for Trustworthy Medical Image Classification	Sep 9, 2023	Conformal PredictionDecision Making	—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