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 201–210 of 424 papers

Title	Date	Tasks	Status	Hype
Interpreting and Correcting Medical Image Classification with PIP-Net	Jul 19, 2023	ClassificationDecision Making	CodeCode Available	1
Systematic comparison of semi-supervised and self-supervised learning for medical image classification	Jul 18, 2023	GPUimage-classification	CodeCode Available	1
M-FLAG: Medical Vision-Language Pre-training with Frozen Language Models and Latent Space Geometry Optimization	Jul 17, 2023	image-classificationImage Classification	CodeCode Available	1
The Whole Pathological Slide Classification via Weakly Supervised Learning	Jul 12, 2023	ClassificationContrastive Learning	—Unverified	0
Learning from Exemplary Explanations	Jul 12, 2023	Classificationimage-classification	—Unverified	0
SPLAL: Similarity-based pseudo-labeling with alignment loss for semi-supervised medical image classification	Jul 10, 2023	Classificationimage-classification	—Unverified	0
Invariant Scattering Transform for Medical Imaging	Jul 7, 2023	Decision MakingDeep Learning	—Unverified	0
The Role of Subgroup Separability in Group-Fair Medical Image Classification	Jul 6, 2023	image-classificationImage Classification	CodeCode Available	0
A ChatGPT Aided Explainable Framework for Zero-Shot Medical Image Diagnosis	Jul 5, 2023	Diagnosticimage-classification	—Unverified	0
More for Less: Compact Convolutional Transformers Enable Robust Medical Image Classification with Limited Data	Jul 1, 2023	Image Captioningimage-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