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/KCL-BMEIS/ScribbleDA

공식 코드

Scribble-based Domain Adaptation via Co-segmentation

https://github.com/KU-CVLAB/CAT-Seg

공식 코드

CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentation

https://github.com/KevinCortacero/Kartezio

공식 코드

Kartezio: Evolutionary Design of Explainable Pipelines for Biomedical Image Analysis

https://github.com/KrishnaswamyLab/DiffKillR

공식 코드

DiffKillR: Killing and Recreating Diffeomorphisms for Cell Annotation in Dense Microscopy Images

https://github.com/KyanChen/RSPrompter

공식 코드

RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model

https://github.com/LARC-CMU-SMU/FoodSeg103-Benchmark-v1

공식 코드

A Large-Scale Benchmark for Food Image Segmentation

https://github.com/LIDS-UNICAMP/DISF

공식 코드

Superpixel Segmentation using Dynamic and Iterative Spanning Forest

https://github.com/LIVIAETS/extended_logbarrier

공식 코드

Constrained Deep Networks: Lagrangian Optimization via Log-Barrier Extensions

https://github.com/LanHEU/CTS

공식 코드

CTS: A Consistency-Based Medical Image Segmentation Model

https://github.com/Levishery/Flywire-Neuron-Tracing

공식 코드

Learning Multimodal Volumetric Features for Large-Scale Neuron Tracing

https://github.com/LiuTingWed/CriDiff

공식 코드

CriDiff: Criss-cross Injection Diffusion Framework via Generative Pre-train for Prostate Segmentation

https://github.com/LongchaoDa/SegmentAsYouWish

공식 코드

Segment as You Wish -- Free-Form Language-Based Segmentation for Medical Images

https://github.com/Luffy03/DCA

공식 코드

Deep Covariance Alignment for Domain Adaptive Remote Sensing Image Segmentation

https://github.com/Lydorn/Polygonization-by-Frame-Field-Learning

공식 코드

Polygonal Building Segmentation by Frame Field Learning

https://github.com/MASILab/SynSeg-Net

공식 코드

SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth

https://github.com/MECLabTUDA/Lifelong-nnUNet

공식 코드

Continual Hippocampus Segmentation with Transformers

https://github.com/MECLabTUDA/M3D-NCA

공식 코드

M3D-NCA: Robust 3D Segmentation with Built-in Quality Control

https://github.com/MIC-DKFZ/MedNeXt

공식 코드

MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation

https://github.com/MIC-DKFZ/nnDetection

공식 코드

nnDetection: A Self-configuring Method for Medical Object Detection

https://github.com/MIC-DKFZ/nnunet

공식 코드

nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation

https://github.com/MIPT-Oulu/mCTSegmentation

공식 코드

Deep-Learning for Tidemark Segmentation in Human Osteochondral Tissues Imaged with Micro-computed Tomography

https://github.com/MansoorehMontazerin/Livelayer-Software

공식 코드

Livelayer: A Semi-Automatic Software Program for Segmentation of Layers and Diabetic Macular Edema in Optical Coherence Tomography Images

https://github.com/MarvinLer/tcga_segmentation

공식 코드

Weakly supervised multiple instance learning histopathological tumor segmentation

https://github.com/MarvinTeichmann/ConvCRF

공식 코드

Convolutional CRFs for Semantic Segmentation

https://github.com/MathLee/OLBPNet4PFOS

공식 코드

Personal Fixations-Based Object Segmentation with Object Localization and Boundary Preservation

https://github.com/McGregorWwww/UDTransNet

공식 코드

Narrowing the semantic gaps in U-Net with learnable skip connections: The case of medical image segmentation

https://github.com/MengzhangLI/ASNG

공식 코드

Automatic Data Augmentation for 3D Medical Image Segmentation

https://github.com/Merrical/PADL

공식 코드

Modeling Annotator Preference and Stochastic Annotation Error for Medical Image Segmentation

https://github.com/Merrical/TAB

공식 코드

Transformer-based Annotation Bias-aware Medical Image Segmentation

https://github.com/MichaelFan01/STDC-Seg

공식 코드

Rethinking BiSeNet For Real-time Semantic Segmentation

https://github.com/MinaJf/DRU-net

공식 코드

DRU-net: An Efficient Deep Convolutional Neural Network for Medical Image Segmentation

https://github.com/MinaJf/FU-net

공식 코드

FU-net: Multi-class Image Segmentation Using Feedback Weighted U-net

https://github.com/Moshtaf/shift-invariant-unet

공식 코드

Investigating Shift-Variance of Convolutional Neural Networks in Ultrasound Image Segmentation

https://github.com/MotohisaFukuda/CROP

공식 코드

Central object segmentation by deep learning for fruits and other roundish objects

https://github.com/MrGiovanni/Nested-UNet

공식 코드

UNet++: A Nested U-Net Architecture for Medical Image Segmentation

https://github.com/MrGiovanni/UNetPlusPlus

공식 코드

UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation

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