https://github.com/mungomeng/survival-xsurv
공식 코드Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck Cancer
Papers with Code 아카이브
Tumor Segmentation
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Tumor Segmentation is the task of identifying the spatial location of a tumor. It is a pixel-level prediction where each pixel is classified as a tumor or background. The most popular benchmark for this task is the BraTS dataset. The models are typically evaluated with the Dice Score metric.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/nasim-aust/braintumor-segmentation-3dunet-brats
공식 코드Brain Tumor Segmentation using Enhanced U-Net Model with Empirical Analysis
https://github.com/neheller/kits19
공식 코드https://github.com/neheller/kits21
공식 코드https://github.com/nubagcilab/pediatric-brain-tumor-segmentation-model
공식 코드https://github.com/oulu-imeds/singr
공식 코드SiNGR: Brain Tumor Segmentation via Signed Normalized Geodesic Transform Regression
https://github.com/ouyangjiahong/representation-disentanglement
공식 코드Representation Disentanglement for Multi-modal brain MR Analysis
https://github.com/ovo1111/jointimagegeneration
공식 코드GuideGen: A Text-Guided Framework for Full-torso Anatomy and CT Volume Generation
https://github.com/pancakewaffles/metasrgan-tutorial
공식 코드https://github.com/pashtari/factorizer
공식 코드Factorizer: A Scalable Interpretable Approach to Context Modeling for Medical Image Segmentation
https://github.com/pfriedri/cwdm
공식 코드cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image Synthesis
https://github.com/project-lighter/lighter
공식 코드https://github.com/project-monai/research-contributions
공식 코드https://github.com/prowontheus/isd-mae
공식 코드Intensity-Spatial Dual Masked Autoencoder for Multi-Scale Feature Learning in Chest CT Segmentation
https://github.com/python-arch/biombz-optimizing-brain-tumor-segmentation-with-mednext-brats-2024-ssa-and-pediatrics
공식 코드Optimizing Brain Tumor Segmentation with MedNeXt: BraTS 2024 SSA and Pediatrics
https://github.com/qdaiing/fedmema
공식 코드https://github.com/qinglyu0828/softdropconnect
공식 코드https://github.com/qinglyu0828/whole-brain-mri_metastases_cls
공식 코드https://github.com/quanato607/xlstm-hved
공식 코드https://github.com/ragmeh11/qu-brats
공식 코드https://github.com/raidionics/raidionics
공식 코드https://github.com/ramtin-mojtahedi/ovtps
공식 코드Towards Optimal Patch Size in Vision Transformers for Tumor Segmentation
https://github.com/raun1/ASC-NET
공식 코드ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly Segmentation
https://github.com/razeineldin/DeepSeg
공식 코드https://github.com/reasat/mstt
공식 코드MSTT-199: MRI Dataset for Musculoskeletal Soft Tissue Tumor Segmentation
https://github.com/reeive/rehydil
공식 코드https://github.com/renato145/posthocOS
공식 코드https://github.com/rezazad68/smunet
공식 코드SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalities
https://github.com/riced2klab/brainnet
공식 코드Glioblastoma Tumor Segmentation using an Ensemble of Vision Transformers
https://github.com/rixez/brats21_kaist_mri_lab
공식 코드https://github.com/rsinghlab/gnn-tumor-seg
공식 코드A Joint Graph and Image Convolution Network for Automatic Brain Tumor Segmentation
https://github.com/satrajitgithub/nrg_ai_neuroonco_preproc
공식 코드https://github.com/satrajitgithub/nrg_ai_neuroonco_segment
공식 코드https://github.com/schuture/meta-uscl
공식 코드Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive Learning
https://github.com/scut-cszcl/sfusion
공식 코드SFusion: Self-attention based N-to-One Multimodal Fusion Block
https://github.com/sepidehamiri/pet_seg_unet
공식 코드Improved automated lesion segmentation in whole-body FDG/PET-CT via Test-Time Augmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.