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Liver Segmentation

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Captain-Hong/UDA-via-joint-adversarial-learning-and-self-learning

공식 코드

Unsupervised domain adaptation for cross-modality liver segmentation via joint adversarial learning and self-learning

https://github.com/Confusezius/unet-lits-2d-pipeline

공식 코드

Liver Lesion Segmentation with slice-wise 2D Tiramisu and Tversky loss function

https://github.com/JLiangLab/SemanticGenesis

공식 코드

Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration

https://github.com/L0SG/grouped-ssd-pytorch

공식 코드

Robust End-to-End Focal Liver Lesion Detection using Unregistered Multiphase Computed Tomography Images

https://github.com/MASILab/ALARM

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Fully Automatic Liver Attenuation Estimation Combing CNN Segmentation and Morphological Operations

https://github.com/MarielleJansen/Liver-metastases-detection

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Liver segmentation and metastases detection in MR images using convolutional neural networks

https://github.com/MarielleJansen/Liver-segmentation

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Optimal input configuration of dynamic contrast enhanced MRI in convolutional neural networks for liver segmentation

https://github.com/MittaDhanunjaya/Master-Thesis

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Upgraded W-Net with Attention Gates and its Application in Unsupervised 3D Liver Segmentation

https://github.com/MrGiovanni/ModelsGenesis

공식 코드

Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis

https://github.com/Tencent/MedicalNet

공식 코드

Med3D: Transfer Learning for 3D Medical Image Analysis

https://github.com/andreped/livermask

공식 코드

Robust End-to-End Focal Liver Lesion Detection using Unregistered Multiphase Computed Tomography Images

https://github.com/ashstuff/dissm

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Deep Implicit Statistical Shape Models for 3D Medical Image Delineation

https://github.com/bbbbbbzhou/APA2Seg-Net

공식 코드

Anatomy-guided Multimodal Registration by Learning Segmentation without Ground Truth: Application to Intraprocedural CBCT/MR Liver Segmentation and Registration

https://github.com/christophreich1996/oss-net

공식 코드

OSS-Net: Memory Efficient High Resolution Semantic Segmentation of 3D Medical Data

https://github.com/cshwhale/Med3D

공식 코드

Med3D: Transfer Learning for 3D Medical Image Analysis

https://github.com/debeshjha/pvtformer

공식 코드

CT Liver Segmentation via PVT-based Encoding and Refined Decoding

https://github.com/jeya-maria-jose/KiU-Net-pytorch

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KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation

https://github.com/junzengz/rmamamba

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A Reverse Mamba Attention Network for Pathological Liver Segmentation

https://github.com/lingchm/medassist-liver-cancer

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SmoothSegNet: A Global-Local Framework for Liver Tumor Segmentation with Clinical KnowledgeInformed Label Smoothing

https://github.com/mansoorhayat777/attention-ghostunetplusplus

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Attention GhostUNet++: Enhanced Segmentation of Adipose Tissue and Liver in CT Images

https://github.com/nanyomy/cmmas

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Cross-Modality Multi-Atlas Segmentation via Deep Registration and Label Fusion

https://github.com/neheller/labels18

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Imperfect Segmentation Labels: How Much Do They Matter?

https://github.com/soumickmj/turbolift

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Liver Segmentation using Turbolift Learning for CT and Cone-beam C-arm Perfusion Imaging

https://github.com/ugurdemir/tranformer_liver_segmentation

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Transformer based Generative Adversarial Network for Liver Segmentation

https://github.com/wenruxue/detta

공식 코드

From Denoising Training to Test-Time Adaptation: Enhancing Domain Generalization for Medical Image Segmentation

https://gitlab.com/nct_tso_public/laparoscopic-image-2-image-translation

공식 코드

Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation

https://github.com/HimBegginer/test_liver

The Liver Tumor Segmentation Benchmark (LiTS)

https://github.com/assassint2017/MICCAI-LITS2017

The Liver Tumor Segmentation Benchmark (LiTS)

https://github.com/black0017/MedicalZooPytorch

Med3D: Transfer Learning for 3D Medical Image Analysis

https://github.com/code-implementation1/Code9/tree/main/Unet

H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes

https://github.com/cswin/AWC

Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis

https://github.com/fhaghighi/SemanticGenesis

Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration

https://github.com/fitushar/AI-in-Lung-Health-Benchmarking-Detection-and-Diagnostic-Models-Across-Multiple-CT-Scan-Datasets

Med3D: Transfer Learning for 3D Medical Image Analysis

https://github.com/lee-zq/3DUNet-Pytorch

The Liver Tumor Segmentation Benchmark (LiTS)

https://github.com/marinbenc/medical-polar-training

https://github.com/mlf-core/liver-ct-segmentation

The Liver Tumor Segmentation Benchmark (LiTS)

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.