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-ai/models/blob/master/research/cv/efficientnet-b0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/mindspore-ai/models/tree/master/official/cv/densenet
https://github.com/mindspore-ai/models/tree/master/official/cv/resnet
https://github.com/mindspore-ai/models/tree/master/official/cv/resnet50_quant
https://github.com/mindspore-ai/models/tree/master/official/cv/resnet_thor
https://github.com/mindspore-ai/models/tree/master/research/cv/FaceAttribute
https://github.com/mindspore-ai/models/tree/master/research/cv/res2net
https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/backbone/resnet.py
https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/conv/MBConv.py
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/mindspore-courses/MindSpore-classification
https://github.com/mindspore-courses/heads-on-mindspore/tree/main/1-best-practice/models
https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/densenet.py
https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/efficientnet.py
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/regnet.py
https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/res2net.py
https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/resnet.py
https://github.com/minggli/DenseNet
https://github.com/mingxingtan/efficientnet
https://github.com/miraclewkf/densenet
https://github.com/miramind/efficientnets_pytorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/mlvccn/bmtc_transferattackvid
https://github.com/mminamina/Transfer_Learning_NaimishNet---Keypoints_Detection
https://github.com/mminamina/Transfer_Learning_ResNet18---Keypoints_Detection
https://github.com/mnguyen0226/image-augmentation-dnn-performance
https://github.com/mnikitin/EfficientNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/mobilesec/arcface-tensorflowlite
https://github.com/moddy2024/resnet-9
https://github.com/modelhub-ai/densenet
https://github.com/morganmcg1/stanford-cars
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/morrisxu-driving/video-music_cross-modal_retrival
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/mr-bulb/DL_basic_github
https://github.com/mrkolarik/3d-brain-segmentation
https://github.com/mrzzy/np-dl-assign-1
https://github.com/mszpc/resnet18
https://github.com/muditrastogi/chestai
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.