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 81–90 of 424 papers

Title	Date	Tasks	Status	Hype	Score
CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning	Apr 18, 2025	Common Sense Reasoningimage-classification	CodeCode Available	1	5
Are Natural Domain Foundation Models Useful for Medical Image Classification?	Oct 30, 2023	image-classificationImage Classification	CodeCode Available	1	5
Enhancing Network Initialization for Medical AI Models Using Large-Scale, Unlabeled Natural Images	Aug 15, 2023	ClassificationDiagnostic	CodeCode Available	1	5
Only Positive Cases: 5-fold High-order Attention Interaction Model for Skin Segmentation Derived Classification	Nov 27, 2023	Image SegmentationLesion Classification	CodeCode Available	1	5
Evolutionary Neural AutoML for Deep Learning	Feb 18, 2019	AutoMLDeep Learning	CodeCode Available	1	5
Evaluating histopathology transfer learning with ChampKit	Jun 14, 2022	BenchmarkingCell Detection	CodeCode Available	1	5
Achieving Fairness Through Channel Pruning for Dermatological Disease Diagnosis	May 14, 2024	Fairnessimage-classification	CodeCode Available	1	5
FACMIC: Federated Adaptative CLIP Model for Medical Image Classification	Oct 8, 2024	Domain AdaptationFederated Learning	CodeCode Available	1	5
Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label Correction	Sep 11, 2021	ClassificationDeep Learning	CodeCode Available	1	5
Tensor Networks for Medical Image Classification	Apr 21, 2020	BIG-bench Machine LearningClassification	CodeCode Available	1	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