Papers with Code 아카이브

Image Segmentation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 1
평가표: 13
보존된 평가 기록: 34

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/mvme-hbut/oreinst

공식 코드

Efficient Segmentation with Texture in Ore Images Based on Box-supervised Approach

https://github.com/naamiinepal/medvlsm

공식 코드

Exploring Transfer Learning in Medical Image Segmentation using Vision-Language Models

https://github.com/naamiinepal/vlsm-adapter

공식 코드

VLSM-Adapter: Finetuning Vision-Language Segmentation Efficiently with Lightweight Blocks

https://github.com/nabsabraham/focal-tversky-unet

공식 코드

A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation

https://github.com/nachifur/LLPC

공식 코드

Local Label Point Correction for Edge Detection of Overlapping Cervical Cells

https://github.com/nadir121/Sat_DA

공식 코드

Improving land cover segmentation across satellites using domain adaptation

https://github.com/nadja1611/lifting-based-variational-multiclass-segmentation

공식 코드

Lifting-based variational multiclass segmentation algorithm: design, convergence analysis, and implementation with applications in medical imaging

https://github.com/naisops/CTranS

공식 코드

ConvTransSeg: A Multi-resolution Convolution-Transformer Network for Medical Image Segmentation

https://github.com/nankepan/VIPMT

공식 코드

Multi-grained Temporal Prototype Learning for Few-shot Video Object Segmentation

https://github.com/nanyomy/cmmas

공식 코드

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

https://github.com/nasim-aust/braintumor-segmentation-3dunet-brats

공식 코드

Brain Tumor Segmentation using Enhanced U-Net Model with Empirical Analysis

https://github.com/nataliiamolch/interpret-lesion-unc

공식 코드

Interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis

https://github.com/naver-ai/maskris

공식 코드

MaskRIS: Semantic Distortion-aware Data Augmentation for Referring Image Segmentation

https://github.com/naver-ai/zim

공식 코드

ZIM: Zero-Shot Image Matting for Anything

https://github.com/naver/oasis

공식 코드

On the Road to Online Adaptation for Semantic Image Segmentation

https://github.com/nayutayuki/lightweight-nmode-decoders-for-u-like-networks

공식 코드

A Lightweight U-like Network Utilizing Neural Memory Ordinary Differential Equations for Slimming the Decoder

https://github.com/ncclabsustech/ifcis-55

공식 코드

Immunofluorescence Capillary Imaging Segmentation: Cases Study

https://github.com/neerakara/test-time-adaptable-neural-networks-for-domain-generalization

공식 코드

Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation

https://github.com/negin-ghamsarian/transformation-invariant-self-training-miccai23

공식 코드

Domain Adaptation for Medical Image Segmentation using Transformation-Invariant Self-Training

https://github.com/neuro-ml/domain_shift_anatomy

공식 코드

Anatomy of Domain Shift Impact on U-Net Layers in MRI Segmentation

https://github.com/neuro-ml/inverse_weighting

공식 코드

Universal Loss Reweighting to Balance Lesion Size Inequality in 3D Medical Image Segmentation

https://github.com/neuroailab/eisen

공식 코드

Unsupervised Segmentation in Real-World Images via Spelke Object Inference

https://github.com/neuropoly/deep-active-learning

공식 코드

Deep Active Learning for Axon-Myelin Segmentation on Histology Data

https://github.com/neuropoly/ivado-medical-imaging

공식 코드

Automatic segmentation of spinal multiple sclerosis lesions: How to generalize across MRI contrasts?

https://github.com/ngaggion/HybridGNet

공식 코드

Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis

https://github.com/ngaggion/chest-xray-landmark-dataset

공식 코드

Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis

https://github.com/ngaggion/ubd_sourcecode

공식 코드

Unsupervised bias discovery in medical image segmentation

https://github.com/nibtehaz/MultiResUNet

공식 코드

MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation

https://github.com/niccolocavagnero/pem

공식 코드

PEM: Prototype-based Efficient MaskFormer for Image Segmentation

https://github.com/nickeopti/bach-contrastive-segmentation

공식 코드

Efficient Self-Supervision using Patch-based Contrastive Learning for Histopathology Image Segmentation

https://github.com/nikhilroxtomar/fanet

공식 코드

FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation

https://github.com/niko-k98/awesome-list-federated-learning-review

공식 코드

Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimation

https://github.com/niloide/implicit_segmentation

공식 코드

NISF: Neural Implicit Segmentation Functions

https://github.com/nina-weng/shortcut_skinseg

공식 코드

Shortcut Learning in Medical Image Segmentation

https://github.com/nitr098/attswinunet

공식 코드

Attention Swin U-Net: Cross-Contextual Attention Mechanism for Skin Lesion Segmentation

https://github.com/nitr098/awesome-u-net

공식 코드

Medical Image Segmentation Review: The success of U-Net

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