Medical Image Classification
123 papers with code • 7 benchmarks • 10 datasets
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.
Benchmarks
These leaderboards are used to track progress in Medical Image Classification
Libraries
Use these libraries to find Medical Image Classification models and implementationsDatasets
ImageNet
OASIS
IDRiD
NCT-CRC-HE-100K
LIMUC
NIH-CXR-LT
MIMIC-CXR-LT
Most implemented papers
Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images
iversonicter/Learn-to-pay-attention
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AGs can be easily integrated into standard CNN models such as VGG or U-Net architectures with minimal computational overhead while increasing the model sensitivity and prediction accuracy.
Tensor Networks for Medical Image Classification
raghavian/loTeNet_pytorch
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MIDL 2019
With the increasing adoption of machine learning tools like neural networks across several domains, interesting connections and comparisons to concepts from other domains are coming to light.
DenseNet for Breast Tumor Classification in Mammographic Images
MindSpore-paper-code-2/code399
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Thus, the aim of this study is to build a deep convolutional neural network method for automatic detection, segmentation, and classification of breast lesions in mammography images.
SKID: Self-Supervised Learning for Knee Injury Diagnosis from MRI Data
sadimanna/skid
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The downstream task in our paper is a class imbalanced multi-label classification.
Rethinking Transfer Learning for Medical Image Classification
sun-umn/ttl
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Transfer learning (TL) from pretrained deep models is a standard practice in modern medical image classification (MIC).
Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label Correction
With the development of deep learning, medical image classification has been significantly improved.
On Fairness of Medical Image Classification with Multiple Sensitive Attributes via Learning Orthogonal Representations
ubc-tea/fcro-fair-classification-orthogonal-representation
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In this paper, we propose a novel method for fair representation learning with respect to multi-sensitive attributes.
Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging
In this work, we evaluated the effect of privacy-preserving training of AI models regarding accuracy and fairness compared to non-private training.
Understanding Silent Failures in Medical Image Classification
Based on the result that none of the benchmarked CSFs can reliably prevent silent failures, we conclude that a deeper understanding of the root causes of failures in the data is required.
Enhancing Network Initialization for Medical AI Models Using Large-Scale, Unlabeled Natural Images
In this study, we explored if SSL for pre-training on non-medical images can be applied to chest radiographs and how it compares to supervised pre-training on non-medical images and on medical images.
