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/MindSpore-paper-code-3/code10/tree/main/glore_res
https://github.com/MindSpore-paper-code-3/code2/tree/main/darknet53
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/MindSpore-paper-code-3/code3/tree/main/FaceAttribute
https://github.com/MindSpore-paper-code-3/code5/tree/main/ResNeXt
https://github.com/MindSpore-paper-code-3/code5/tree/main/res2net_deeplabv3
https://github.com/MindSpore-paper-code-3/code5/tree/main/resnet50_adv_pruning
https://github.com/MindSpore-paper-code-3/code7/tree/main/FaceAttribute
https://github.com/MindSpore-paper-code-3/code8/tree/main/FaceAttribute
https://github.com/MindSpore-paper-code-3/code8/tree/main/darknet53
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/MindSpore-paper-code-3/code8/tree/main/efficientnet-b0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/MindSpore-scientific/code-14/tree/main/res_mlp_ms
https://github.com/MioChiu/ResNet_TensorFlow
https://github.com/MishaLaskin/vq-vae
https://github.com/MishaLaskin/vqvae
https://github.com/Mnpr/OC-DeepLearning
https://github.com/Mnpr/Thoractic-Diseases-Detection
https://github.com/Mnpr/Thoractic_Disease_Detection
https://github.com/MrRiahi/Convolutional-Neural-Networks-Tensorflow
https://github.com/NASA-NeMO-Net/NeMO-Net
https://github.com/NVIDIA/DeepLearningExamples
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/NVIDIA/retinanet-examples
https://github.com/OlafenwaMoses/Action-Net
https://github.com/OlafenwaMoses/IdenProf
https://github.com/OlafenwaMoses/Traffic-Net
https://github.com/Olayemiy/Image-Classification-With-Resnet
https://github.com/PPPrior/i3d-pytorch
https://github.com/PaddlePaddle/PaddleClas
https://github.com/PaddlePaddle/PaddleDetection
https://github.com/PlumedSerpent/tmp_perspective_map
https://github.com/PotatoSpudowski/CactiNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Punakshi/Deep-Residual-Learning-for-Image-Recognition-Implementation
https://github.com/RRoundTable/OceanLitter_dataset_generator
https://github.com/RaghavHub/rekuten_hack
https://github.com/RajdeepBiswas/Manufacturing-Quality-Inspection
https://github.com/RajuGudhe/Cancer_Tissue_Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.