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Medical Image Classification

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하위 태스크: 1
평가표: 11
보존된 평가 기록: 20

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.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/vita-group/longtailcxr

공식 코드

Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study

https://github.com/vita-group/prunecxr

공식 코드

How Does Pruning Impact Long-Tailed Multi-Label Medical Image Classifiers?

https://github.com/wnn2000/fediic

공식 코드

FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image Classification

https://github.com/wnn2000/ffl4mia

공식 코드

Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State Matching

https://github.com/wurenkai/MHA-UNet

공식 코드

Only Positive Cases: 5-fold High-order Attention Interaction Model for Skin Segmentation Derived Classification

https://github.com/xmed-lab/fed-mas

공식 코드

Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image Classification

https://github.com/xmed-lab/fopro-kd

공식 코드

FoPro-KD: Fourier Prompted Effective Knowledge Distillation for Long-Tailed Medical Image Recognition

https://github.com/xrad-ulm/proto-caps

공식 코드

Interpretable Medical Image Classification using Prototype Learning and Privileged Information

https://github.com/xzheng0427/xfmamba

공식 코드

XFMamba: Cross-Fusion Mamba for Multi-View Medical Image Classification

https://github.com/yXiangXiong/ParaGAN

공식 코드

A Parameterized Generative Adversarial Network Using Cyclic Projection for Explainable Medical Image Classification

https://github.com/yaoppeng/topoclassification

공식 코드

PHG-Net: Persistent Homology Guided Medical Image Classification

https://github.com/yaoyaozhu19/bsda

공식 코드

BSDA: Bayesian Random Semantic Data Augmentation for Medical Image Classification

https://github.com/yijinhuang/fpt

공식 코드

Fine-grained Prompt Tuning: A Parameter and Memory Efficient Transfer Learning Method for High-resolution Medical Image Classification

https://github.com/yjlee22/kan-ecoc

공식 코드

Improving Generalizability of Kolmogorov-Arnold Networks via Error-Correcting Output Codes

https://github.com/ytongxie/pairaug

공식 코드

PairAug: What Can Augmented Image-Text Pairs Do for Radiology?

https://github.com/ytongxie/unimiss-code

공식 코드

UniMiSS: Universal Medical Self-Supervised Learning via Breaking Dimensionality Barrier

https://github.com/yueliukth/patchdropout

공식 코드

PatchDropout: Economizing Vision Transformers Using Patch Dropout

https://github.com/yuhaozhang/convirt

공식 코드

Contrastive Learning of Medical Visual Representations from Paired Images and Text

https://github.com/yuweisunn/homogeneous-learning

공식 코드

Homogeneous Learning: Self-Attention Decentralized Deep Learning

https://github.com/z-sherkat/baytta

공식 코드

BayTTA: Uncertainty-aware medical image classification with optimized test-time augmentation using Bayesian model averaging

https://github.com/zeeshannisar/cx_gan

공식 코드

Counterfactual Explanation and Instance-Generation using Cycle-Consistent Generative Adversarial Networks

https://github.com/zjysteven/mixoe

공식 코드

Mixture Outlier Exposure: Towards Out-of-Distribution Detection in Fine-grained Environments

https://github.com/zuwenqiang/ept

공식 코드

Embedded Prompt Tuning: Towards Enhanced Calibration of Pretrained Models for Medical Images

https://github.com/0492wzl/tensorflow_slim_densenet

Densely Connected Convolutional Networks

https://github.com/0jason000/DenseNet

Densely Connected Convolutional Networks

https://github.com/1044197988/TF.Keras-Commonly-used-models

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

https://github.com/2023-MindSpore-1/ms-code-146

Densely Connected Convolutional Networks

https://github.com/2023-MindSpore-1/ms-code-151

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/2023-MindSpore-1/ms-code-16/tree/main/3D_DenseNet

Densely Connected Convolutional Networks

https://github.com/2023-MindSpore-1/ms-code-177

Deep Residual Learning for Image Recognition

https://github.com/2023-MindSpore-1/ms-code-18/tree/main/lresnet100e_ir

Deep Residual Learning for Image Recognition

https://github.com/2023-MindSpore-1/ms-code-189/tree/main/modelarts

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/2023-MindSpore-1/ms-code-195

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/2023-MindSpore-1/ms-code-201

Deep Residual Learning for Image Recognition

https://github.com/2023-MindSpore-1/ms-code-208

Deep Residual Learning for Image Recognition

https://github.com/2023-MindSpore-1/ms-code-213/tree/main/FaceAttribute

Deep Residual Learning for Image Recognition

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