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 21–30 of 424 papers

Title	Date	Tasks	Status	Hype
Counterfactual Explanations for Medical Image Classification and Regression using Diffusion Autoencoder	Aug 2, 2024	counterfactualimage-classification	CodeCode Available	1
BiasPruner: Debiased Continual Learning for Medical Image Classification	Jul 11, 2024	ClassificationContinual Learning	CodeCode Available	1
Embedded Prompt Tuning: Towards Enhanced Calibration of Pretrained Models for Medical Images	Jul 1, 2024	Cross-Domain Few-Shotimage-classification	CodeCode Available	1
FedMLP: Federated Multi-Label Medical Image Classification under Task Heterogeneity	Jun 27, 2024	Federated Learningimage-classification	CodeCode Available	1
Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling	May 14, 2024	image-classificationImage Classification	CodeCode Available	1
Achieving Fairness Through Channel Pruning for Dermatological Disease Diagnosis	May 14, 2024	Fairnessimage-classification	CodeCode Available	1
Pseudo-Prompt Generating in Pre-trained Vision-Language Models for Multi-Label Medical Image Classification	May 10, 2024	Decoderimage-classification	CodeCode Available	1
Rethinking model prototyping through the MedMNIST+ dataset collection	Apr 24, 2024	Image ClassificationMedical Image Classification	CodeCode Available	1
PairAug: What Can Augmented Image-Text Pairs Do for Radiology?	Apr 7, 2024	Data Augmentationimage-classification	CodeCode Available	1
Fine-grained Prompt Tuning: A Parameter and Memory Efficient Transfer Learning Method for High-resolution Medical Image Classification	Mar 12, 2024	image-classificationImage Classification	CodeCode Available	1

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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