Papers with Code 아카이브
Image Segmentation
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Image Segmentation is a computer vision task that involves dividing an image into multiple segments or regions, each of which corresponds to a different object or part of an object. The goal of image segmentation is to assign a unique label or category to each pixel in the image, so that pixels with similar attributes are grouped together.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/cchen-cc/ma-sam
공식 코드MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation
https://github.com/cchen-cc/sfda-dpl
공식 코드Source-Free Domain Adaptive Fundus Image Segmentation with Denoised Pseudo-Labeling
https://github.com/cerebras/online-normalization
공식 코드https://github.com/cgpxy/baaf
공식 코드https://github.com/cgpxy/nu-net
공식 코드Rethinking the Unpretentious U-net for Medical Ultrasound Image Segmentation
https://github.com/cgpzy/nu-net
공식 코드Rethinking the Unpretentious U-net for Medical Ultrasound Image Segmentation
https://github.com/chamanis/splitfedzip
공식 코드SplitFedZip: Learned Compression for Data Transfer Reduction in Split-Federated Learning
https://github.com/chandagrover/simsam
공식 코드https://github.com/chaofanma/fusioner
공식 코드Open-vocabulary Semantic Segmentation with Frozen Vision-Language Models
https://github.com/chaoningzhang/mobilesam
공식 코드Faster Segment Anything: Towards Lightweight SAM for Mobile Applications
https://github.com/chaosallen/IPNV2_pytorch
공식 코드OCTA-500: A Retinal Dataset for Optical Coherence Tomography Angiography Study
https://github.com/chaostheproducer/ttt4d
공식 코드https://github.com/charlespikachu/mcibi
공식 코드Mining Contextual Information Beyond Image for Semantic Segmentation
https://github.com/charlesyou999648/MONA
공식 코드Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels
https://github.com/charlesyou999648/action
공식 코드ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical Contrast
https://github.com/charlesyou999648/arco
공식 코드Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective
https://github.com/charlesyou999648/morse
공식 코드Implicit Anatomical Rendering for Medical Image Segmentation with Stochastic Experts
https://github.com/charrywhite/elastic_interaction_based_loss
공식 코드An Elastic Interaction-Based Loss Function for Medical Image Segmentation
https://github.com/charulp2499/fess_loss
공식 코드FESS Loss: Feature-Enhanced Spatial Segmentation Loss for Optimizing Medical Image Analysis
https://github.com/charzharr/miccai23-swipe-implicit-segmentation
공식 코드SwIPE: Efficient and Robust Medical Image Segmentation with Implicit Patch Embeddings
https://github.com/chen-ziyang/grata
공식 코드Gradient Alignment Improves Test-Time Adaptation for Medical Image Segmentation
https://github.com/chen-ziyang/trid
공식 코드https://github.com/chen-ziyang/vptta
공식 코드https://github.com/cheneyxuyimin/sam2dino-seg
공식 코드https://github.com/cheng-01037/Self-supervised-Fewshot-Medical-Image-Segmentation
공식 코드Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation
https://github.com/cheng-01037/causality-medical-image-domain-generalization
공식 코드Causality-inspired Single-source Domain Generalization for Medical Image Segmentation
https://github.com/cheng-01037/probability_calibration_for_ood_mri_segmentation
공식 코드Improved post-hoc probability calibration for out-of-domain MRI segmentation
https://github.com/chengyinlee/autoprosam_2024
공식 코드AutoProSAM: Automated Prompting SAM for 3D Multi-Organ Segmentation
https://github.com/chengyinlee/focalunetr
공식 코드FocalUNETR: A Focal Transformer for Boundary-aware Segmentation of CT Images
https://github.com/chengyinlee/mulmodseg_2024
공식 코드https://github.com/chenhong-zhou/OM-Net
공식 코드One-pass Multi-task Networks with Cross-task Guided Attention for Brain Tumor Segmentation
https://github.com/chenjzbuaa/satir
공식 코드https://github.com/chenxi116/TF-phrasecut-public
공식 코드Recurrent Multimodal Interaction for Referring Image Segmentation
https://github.com/cherise215/CardiacMRSegmentation
공식 코드Improving the generalizability of convolutional neural network-based segmentation on CMR images
https://github.com/cherise215/Cooperative_Training_and_Latent_Space_Data_Augmentation
공식 코드Cooperative Training and Latent Space Data Augmentation for Robust Medical Image Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.