Medical Image Classification
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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/wusaifei/HWCC_image_classification
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/xcmyz/Face-Binary-Classification
https://github.com/xcmyz/FaceDetection
https://github.com/xcmyz/Face_Binary_Classfication
https://github.com/xhlulu/arxiv-assistant
https://github.com/xiaoyufenfei/LEDNet
https://github.com/xinkuansong/modified-resnet-acc-0.9638-10.7M-parameters
https://github.com/xiuyu0000/papers_with_examples/tree/main/cancer_detection_demo
https://github.com/xiuyu0000/tutorials/tree/main/resnet
https://github.com/xiuyu0000/vision/blob/main/mindvision/classification/models/backbones/efficientnet.py
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/xiuyu0000/vision/blob/main/mindvision/classification/models/resnet.py
https://github.com/xslidi/EfficientNets_ddl_apex
https://github.com/xufanxiong/classification-of-caltech-256
https://github.com/xypan1232/iDeepE
https://github.com/yahoo/open_nsfw
https://github.com/yakhyo/EfficientNet-PyTorch
https://github.com/yakhyo/gaze-estimation
https://github.com/yakhyo/head-pose-estimation
https://github.com/yakhyo/retinaface-pytorch
https://github.com/yangboz/TransferLearning4Dentist
https://github.com/yangorwell/NGPlus
https://github.com/yangyucheng000/University/tree/main/model-3/resnet
https://github.com/yangyucheng000/densenet/blob/main/densenet.py
https://github.com/yangyucheng000/res2net/blob/main/res2net.py
https://github.com/yangyucheng000/resnet_Ascend
https://github.com/yashclone999/ResNet_MODEL
https://github.com/yaziciz/GLIMS
MONAI: An open-source framework for deep learning in healthcare
https://github.com/ycao5602/SAL
https://github.com/yfreedomliTHU/Res2Net
https://github.com/yibeiou/Advanced_Business_Intelligence
https://github.com/yichigo/Chest-X-Ray
https://github.com/yingkaisha/keras-unet-collection
https://github.com/yoshitomo-matsubara/torchdistill
https://github.com/youngwoo-yoon/Practical-tips-for-lightweight-deep-learning
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/yudhisteer/Face-Recognition-with-Masks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.