Medical Image Classification
122 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
MIMIC-CXR-LT
NIH-CXR-LT
Most implemented papers
Deep Residual Learning for Image Recognition
tensorflow/models
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CVPR 2016
Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
Densely Connected Convolutional Networks
liuzhuang13/DenseNet
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CVPR 2017
Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output.
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available.
Res2Net: A New Multi-scale Backbone Architecture
rwightman/pytorch-image-models
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We evaluate the Res2Net block on all these models and demonstrate consistent performance gains over baseline models on widely-used datasets, e. g., CIFAR-100 and ImageNet.
RegNet: Self-Regulated Network for Image Classification
keras-team/keras
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The ResNet and its variants have achieved remarkable successes in various computer vision tasks.
Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation
LeeJunHyun/Image_Segmentation
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In this paper, we propose a Recurrent Convolutional Neural Network (RCNN) based on U-Net as well as a Recurrent Residual Convolutional Neural Network (RRCNN) based on U-Net models, which are named RU-Net and R2U-Net respectively.
ResNet strikes back: An improved training procedure in timm
rwightman/pytorch-image-models
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NeurIPS Workshop ImageNet_PPF 2021
We share competitive training settings and pre-trained models in the timm open-source library, with the hope that they will serve as better baselines for future work.
Contrastive Learning of Medical Visual Representations from Paired Images and Text
Existing work commonly relies on fine-tuning weights transferred from ImageNet pretraining, which is suboptimal due to drastically different image characteristics, or rule-based label extraction from the textual report data paired with medical images, which is inaccurate and hard to generalize.
Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification
Optimization-AI/LibAUC
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ICCV 2021
Our studies demonstrate that the proposed DAM method improves the performance of optimizing cross-entropy loss by a large margin, and also achieves better performance than optimizing the existing AUC square loss on these medical image classification tasks.
DaViT: Dual Attention Vision Transformers
dingmyu/davit
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We show that these two self-attentions complement each other: (i) since each channel token contains an abstract representation of the entire image, the channel attention naturally captures global interactions and representations by taking all spatial positions into account when computing attention scores between channels; (ii) the spatial attention refines the local representations by performing fine-grained interactions across spatial locations, which in turn helps the global information modeling in channel attention.
