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 361–370 of 424 papers

Title	Date	Tasks	Status
Variational Knowledge Distillation for Disease Classification in Chest X-Rays	Mar 19, 2021	ClassificationGeneral Classification	—Unverified
Transfer learning for automatic brain tumor classification Using MRI Images.	Mar 19, 2021	Brain Tumor ClassificationBrain Tumor Segmentation	—Unverified
TransMed: Transformers Advance Multi-modal Medical Image Classification	Mar 10, 2021	ClassificationGeneral Classification	—Unverified
Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation	Feb 28, 2021	BenchmarkingGeneral Classification	—Unverified
Highly Efficient Representation and Active Learning Framework and Its Application to Imbalanced Medical Image Classification	Feb 25, 2021	Active LearningClassification	—Unverified
PRICURE: Privacy-Preserving Collaborative Inference in a Multi-Party Setting	Feb 19, 2021	Collaborative Inferenceimage-classification	CodeCode Available
Flow-Mixup: Classifying Multi-labeled Medical Images with Corrupted Labels	Feb 9, 2021	image-classificationImage Classification	—Unverified
Multi-Instance Learning by Utilizing Structural Relationship among Instances	Feb 3, 2021	Graph Attentionimage-classification	—Unverified
DenseNet for Breast Tumor Classification in Mammographic Images	Jan 24, 2021	ClassificationDiagnostic	CodeCode Available
Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations	Jan 20, 2021	Cancer-no cancer per image classificationGPU	—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