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 351–360 of 424 papers

Title	Date	Tasks	Status	Hype
PRICURE: Privacy-Preserving Collaborative Inference in a Multi-Party Setting	Feb 19, 2021	Collaborative Inferenceimage-classification	CodeCode Available	0
Flow-Mixup: Classifying Multi-labeled Medical Images with Corrupted Labels	Feb 9, 2021	image-classificationImage Classification	—Unverified	0
Multi-Instance Learning by Utilizing Structural Relationship among Instances	Feb 3, 2021	Graph Attentionimage-classification	—Unverified	0
DenseNet for Breast Tumor Classification in Mammographic Images	Jan 24, 2021	ClassificationDiagnostic	CodeCode Available	0
Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations	Jan 20, 2021	Cancer-no cancer per image classificationGPU	—Unverified	0
Big Self-Supervised Models Advance Medical Image Classification	Jan 13, 2021	ClassificationContrastive Learning	CodeCode Available	1
RegNet: Self-Regulated Network for Image Classification	Jan 3, 2021	ClassificationGeneral Classification	CodeCode Available	4
Application of the Neural Network Dependability Kit in Real-World Environments	Dec 14, 2020	ClassificationGeneral Classification	—Unverified	0
Aggregative Self-Supervised Feature Learning from a Limited Sample	Dec 14, 2020	image-classificationImage Classification	—Unverified	0
Combining Similarity and Adversarial Learning to Generate Visual Explanation: Application to Medical Image Classification	Dec 14, 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