Papers with Code 아카이브
Medical Image Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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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/souravverma94/pre_trained_imgclassifier_dogBreeds
https://github.com/sparvangada/capstone_project
https://github.com/stanleyjzheng/masseyhacksvii
https://github.com/suhas1999/Flip-kart-grid-challenge
https://github.com/sumitkutty/Anti-Spoof-Face-Recognition
https://github.com/sungyoonahn/Hint-based-image-colorization-using-Attention-Unet
https://github.com/sunlizhuang/YOLOv1-PaddlePaddle
https://github.com/sunnysoni97/single_image_fl
https://github.com/swasun/VQ-VAE-images
https://github.com/swetak20/Deep_into_CNNs
https://github.com/swxhss/densenet_tensorflow
https://github.com/syiin/ann_cancer_detection
https://github.com/syiin/resnet_cancer_detection
https://github.com/t0930198/backup
https://github.com/tanjeffreyz/deep-residual-learning
https://github.com/tarikdgny/Optical_Coherence_Tomography
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/tayebiarasteh/llmmed
Enhancing Network Initialization for Medical AI Models Using Large-Scale, Unlabeled Natural Images
https://github.com/taylor-914/DenseNet
https://github.com/tcheung99/ResNet_MiniProject
https://github.com/tensorflow/models
https://github.com/tensorflow/models/tree/master/research/deeplab
https://github.com/tensorflow/models/tree/master/research/slim
https://github.com/tensorpack/tensorpack
https://github.com/tensorpack/tensorpack/tree/master/examples/ResNet
https://github.com/the-super-toys/glimpse-models
https://github.com/theidentity/Improved-GAN-PyTorch
Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images
https://github.com/thughost2/Padam
https://github.com/tiagoCuervo/JapaNet
https://github.com/titu1994/DenseNet
https://github.com/titu1994/keras-efficientnets
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/tjdevWorks/ConVIRT-Federated
Contrastive Learning of Medical Visual Representations from Paired Images and Text
https://github.com/toanbkmt/EfficientnetFruitDetect
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/towhee-io/towhee
https://github.com/trachpro/arcface-tf2
https://github.com/triple7/Keras-WGAN-RGB-128x128
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.