https://github.com/alexssanchez/unet-app-pucp
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
Papers with Code 아카이브
Computed Tomography (CT)
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The term “computed tomography”, or CT, refers to a computerized x-ray imaging procedure in which a narrow beam of x-rays is aimed at a patient and quickly rotated around the body, producing signals that are processed by the machine's computer to generate cross-sectional images—or “slices”—of the body.
<span style="color:grey; opacity: 0.6">( Image credit: Liver Lesion Detection from Weakly-labeled Multi-phase CT Volumes with a Grouped Single Shot MultiBox Detector )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/amobiny/DECAPS_for_COVID19
Radiologist-Level COVID-19 Detection Using CT Scans with Detail-Oriented Capsule Networks
https://github.com/amobiny/Fast_CapsNet
https://github.com/andreped/PLS-Net
Efficient 3D Fully Convolutional Networks for Pulmonary Lobe Segmentation in CT Images
https://github.com/assassint2017/MICCAI-LITS2017
https://github.com/banben/maskrcnn-benchmark
https://github.com/basaltzhang/maskrcnn-benchmark
https://github.com/black0017/MedicalZooPytorch
https://github.com/brack-wang/zeco
X-Field: A Physically Grounded Representation for 3D X-ray Reconstruction
https://github.com/ccchang1023/maskrcnn-benchmark
https://github.com/code-implementation1/Code1/tree/main/dnet_nas
D-Net: Siamese based Network with Mutual Attention for Volume Alignment
https://github.com/cyctrung/DPnet
https://github.com/darwinai/FibrosisNet
https://github.com/desaisrkr/https-github.com-UCSD-AI4H-COVID-CT
https://github.com/dronaka/DSB2017
Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network
https://github.com/ericoldgren/kex---ct-reconstruction
https://github.com/fizyr/keras-retinanet
Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels
https://github.com/francismontalbo/covid19_diagnosis_and_analysis_of_truncated_dcnns
https://github.com/frgfm/Holocron
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
https://github.com/fsafe/Capstone
3D Context Enhanced Region-based Convolutional Neural Network for End-to-End Lesion Detection
https://github.com/grip-unina/m3dsynth
M3Dsynth: A dataset of medical 3D images with AI-generated local manipulations
https://github.com/haydengunraj/COVIDNet-CT
https://github.com/hjahan58/framing-u-net
Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CT
https://github.com/hxyshare/lungcancerclassifier
Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network
https://github.com/i-sip/COVID-CAPS
COVID-CAPS: A Capsule Network-based Framework for Identification of COVID-19 cases from X-ray Images
https://github.com/ilmimris/bangkit-bgd2a-webapp
https://github.com/jestinas/covid
https://github.com/jongcye/Domain.Adaptation.AcceleratedMR
Deep Learning with Domain Adaptation for Accelerated Projection-Reconstruction MR
https://github.com/jongcye/FramingUNet
Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CT
https://github.com/jongcye/TMI_SwitchableCycleGAN
Continuous Conversion of CT Kernel using Switchable CycleGAN with AdaIN
https://github.com/jonvthvn90/Project
https://github.com/junqiangchen/COVID-CT19-Challenge
https://github.com/jusiro/fewshot-finetuning
Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis
https://github.com/kacel33/fixed-DeepSEED
https://github.com/kimjisoo12/guidednet
https://github.com/kirangpcet/KRCCTumor
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.