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/asprenger/keras_fc_densenet
https://github.com/astoycos/Mini_Project2
https://github.com/atapour/ransomware-classification
https://github.com/aurelien-peden/Deep-Learning-paper-implementations
https://github.com/avocardio/resnet_vs_convnext
https://github.com/axmoyal/Steganalysis-with-CNN
https://github.com/ayushpatidar/PR1-DenseNet
https://github.com/azima-fariha/DeepLearning_DenselyConnectedConvolutionalNetworks
https://github.com/b8goal/PIRL-Residual-network
https://github.com/bamos/densenet.pytorch
https://github.com/barmayo/spatial_attention
https://github.com/bclwan/MRI_Brain_Segmentation
https://github.com/ben-davidson-6/fixup
https://github.com/bentrevett/pytorch-image-classification
https://github.com/bertrandlalo/deep_dog_breeds
https://github.com/bethgelab/InDomainGeneralizationBenchmark
https://github.com/bfortuner/pytorch_tiramisu
https://github.com/bhaskar-gaur/DenseNet-Keras
https://github.com/boris127/vehicle-detection
https://github.com/bozliu/E2E-Keyword-Spotting
https://github.com/brycexu/MR-Residual-Net
https://github.com/buiquangmanhhp1999/age_gender_estimation
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/c1ph3rr/Deep-Residual-Learning-for-Image-Recognition
https://github.com/calmisential/TensorFlow2.0_ResNet
https://github.com/cameronchoi/r3det-copy
https://github.com/canturan10/satellighte
https://github.com/caojoshua/Plastic-Recyclability
https://github.com/captaindario/dakanji-single-kanji-recognition
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/cgebbe/kaggle_pku-autonomous-driving
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/cges60809/DenseNet
https://github.com/chayanchatterjee/cbc-skynet
https://github.com/chayanchatterjee/gw-skylocator
https://github.com/chenghui-666/acl
Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label Correction
https://github.com/chihchenghsieh/multimodal-medical-diagnosis-system
https://github.com/chrhenning/hypercl
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.