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/ChengBinJin/SpineC2M
https://github.com/FluteXu/ms-project
https://github.com/GarkP/LUNA16_Challange
False Positive Reduction in Lung Computed Tomography Images using Convolutional Neural Networks
https://github.com/GorkemP/LUNA16_Challange
False Positive Reduction in Lung Computed Tomography Images using Convolutional Neural Networks
https://github.com/GuoLiuFang/maskrcnn-benchmark-lfs
https://github.com/HastingsGreer/InverseConsistency
$\texttt{GradICON}$: Approximate Diffeomorphisms via Gradient Inverse Consistency
https://github.com/HimBegginer/test_liver
https://github.com/HowardZhang002/MULAN
https://github.com/Hydron063/Rage
Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network
https://github.com/IIT-PAVIS/radiomics4covid19
https://github.com/IliasPap/COVIDNet
https://github.com/Karna4621/dsb_new
Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network
https://github.com/KrissHs/KIT19
https://github.com/Lingfeng158/DSBSeperate
Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network
https://github.com/MGH-LMIC/windows_optimization
Practical Window Setting Optimization for Medical Image Deep Learning
https://github.com/MarioNavarrete/anchor_optimization
Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels
https://github.com/MichaelMedvedskiy/Neuro_add_learning
Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network
https://github.com/MichaelMedvedskiy/Neuro_least_changes
Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network
https://github.com/MichaelMedvedskiy/lung_cancer_modified
Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network
https://github.com/MindSpore-paper-code-3/code8/tree/main/dnet_nas
D-Net: Siamese based Network with Mutual Attention for Volume Alignment
https://github.com/MrGiovanni/Nested-UNet
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
https://github.com/Nariyiel/DSB2017
Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network
https://github.com/NikV-JS/U-Det
U-Det: A Modified U-Net architecture with bidirectional feature network for lung nodule segmentation
https://github.com/PranjalSahu/InteractiveSmoothingDBT
A Cone-Beam X-Ray CT Data Collection designed for Machine Learning
https://github.com/Sempronius/LITS_test
Detection-aided liver lesion segmentation using deep learning
https://github.com/ShahinSHH/COVID-CT-MD
https://github.com/Siddhartha24795/Medical-Image-Synthesis
https://github.com/SilvioGiancola/maskrcnn-benchmark
https://github.com/Stsh4lson/TOM-2020
https://github.com/Subhadip-1/data_driven_convex_regularization
https://github.com/TsaftarisCollaboratory/CSDisentanglement_Metrics_Library
Disentangled Representation Learning in Cardiac Image Analysis
https://github.com/YSerin/TMI_SwitchableCycleGAN
Continuous Conversion of CT Kernel using Switchable CycleGAN with AdaIN
https://github.com/Zhang-Jing-Xuan/MaskRCNN
https://github.com/abonte/protopdebug
BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patients
https://github.com/albertsokol/pneumothorax-detection-unet
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.