Medical Image Classification

98 papers with code • 2 benchmarks • 6 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.


Use these libraries to find Medical Image Classification models and implementations
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Most implemented papers

Deep Residual Learning for Image Recognition

tensorflow/models 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 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

tensorflow/tpu ICML 2019

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 2 Apr 2019

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.

Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation

LeeJunHyun/Image_Segmentation 20 Feb 2018

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 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

yuhaozhang/convirt 2 Oct 2020

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.

RegNet: Self-Regulated Network for Image Classification

keras-team/keras 3 Jan 2021

The ResNet and its variants have achieved remarkable successes in various computer vision tasks.

Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification

Optimization-AI/LibAUC 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.

Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images

iversonicter/Learn-to-pay-attention 22 Aug 2018

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.