Papers with Code 아카이브
Medical Image Segmentation
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Medical Image Segmentation is a computer vision task that involves dividing an medical image into multiple segments, where each segment represents a different object or structure of interest in the image. The goal of medical image segmentation is to provide a precise and accurate representation of the objects of interest within the image, typically for the purpose of diagnosis, treatment planning, and quantitative analysis.
<span style="color:grey; opacity: 0.6">( Image credit: IVD-Net )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mariannerakic/tyche
공식 코드Tyche: Stochastic In-Context Learning for Medical Image Segmentation
https://github.com/marinbenc/medical-polar-training
공식 코드Using the Polar Transform for Efficient Deep Learning-Based Aorta Segmentation in CTA Images
https://github.com/masadcv/fastgeodis
공식 코드https://github.com/masih4/Skin-lesion-segmentation-effects-of-the-classification-perfromnce
공식 코드The Effects of Skin Lesion Segmentation on the Performance of Dermatoscopic Image Classification
https://github.com/masih4/foot_ulcer_segmentation
공식 코드Automatic Foot Ulcer Segmentation Using an Ensemble of Convolutional Neural Networks
https://github.com/masilab/3dux-net
공식 코드https://github.com/masilab/deform-uxnet
공식 코드https://github.com/masilab/influence_of_fusion_on_pancreas_segmentation
공식 코드https://github.com/masilab/repux-net
공식 코드Scaling Up 3D Kernels with Bayesian Frequency Re-parameterization for Medical Image Segmentation
https://github.com/masilab/unest
공식 코드https://github.com/masqm/Faster-Mean-Shift
공식 코드https://github.com/mateuszbuda/brain-segmentation
공식 코드https://github.com/matheus-viana/heterogeneous_sampling
공식 코드https://github.com/mathilde-b/SFDA
공식 코드https://github.com/mathilde-b/tta
공식 코드Test-Time Adaptation with Shape Moments for Image Segmentation
https://github.com/mathildefaanes/us_brain_tumor_segmentation
공식 코드https://github.com/mathpluscode/imgx-diffseg
공식 코드A Recycling Training Strategy for Medical Image Segmentation with Diffusion Denoising Models
https://github.com/mathwrx/BA-Net/blob/main/encoding/models/BANet.py
공식 코드Boundary-aware Context Neural Network for Medical Image Segmentation
https://github.com/matteo-bastico/mi-seg
공식 코드https://github.com/mattiaspaul/TernaryNet
공식 코드https://github.com/mawady/bus-segmentation
공식 코드https://github.com/mawio02/fisrg-for-automated-lesion-after-stroke-segmentation-in-mri
공식 코드https://github.com/maxiao0234/ttga
공식 코드Test-Time Generative Augmentation for Medical Image Segmentation
https://github.com/maxwell0027/lefed
공식 코드https://github.com/mayurmallya/deepguide
공식 코드https://github.com/mazurowski-lab/finetune-sam
공식 코드https://github.com/mazurowski-lab/segment-anything-medical
공식 코드Segment Anything Model for Medical Image Analysis: an Experimental Study
https://github.com/mazurowski-lab/segment-anything-medical-evaluation
공식 코드Segment Anything Model for Medical Image Analysis: an Experimental Study
https://github.com/mazurowski-lab/segmentanybone
공식 코드SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location on MRI
https://github.com/mazurowski-lab/single-image-test-time-adaptation
공식 코드https://github.com/mcgregorwwww/uctransnet
공식 코드https://github.com/mckellwoodland/dimen_reduce_mahal
공식 코드Dimensionality Reduction for Improving Out-of-Distribution Detection in Medical Image Segmentation
https://github.com/mdciri/3D-augmentation-techniques
공식 코드What is the best data augmentation for 3D brain tumor segmentation?
https://github.com/mdciri/augmentation
공식 코드What is the best data augmentation for 3D brain tumor segmentation?
https://github.com/meclabtuda/atlas-replay
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.