Papers with Code 아카이브

Segmentation

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/mrirecon/dl-segmentation-realtime-cmr

공식 코드

Assessment of Deep Learning Segmentation for Real-Time Free-Breathing Cardiac Magnetic Resonance Imaging at Rest and Under Exercise Stress

https://github.com/mrkshllr/bixt

공식 코드

Perceiving Longer Sequences With Bi-Directional Cross-Attention Transformers

https://github.com/mrottmann/MetaSeg

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Uncertainty Measures and Prediction Quality Rating for the Semantic Segmentation of Nested Multi Resolution Street Scene Images

https://github.com/mruil/ddsb

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DDSB: An Unsupervised and Training-free Method for Phase Detection in Echocardiography

https://github.com/msahasrabudhe/miccai2020_self_sup_nuclei_seg

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Self-Supervised Nuclei Segmentation in Histopathological Images Using Attention

https://github.com/mseg-dataset/mseg-semantic

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MSeg: A Composite Dataset for Multi-domain Semantic Segmentation

https://github.com/msiam/mmc-multiscalememory

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Multiscale Memory Comparator Transformer for Few-Shot Video Segmentation

https://github.com/mskcc-computational-pathology/dmmn-ovary

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Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation

https://github.com/msraig/pfcnn

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PFCNN: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames

https://github.com/mszuyx/mosts

공식 코드

A Self-Supervised Miniature One-Shot Texture Segmentation (MOSTS) Model for Real-Time Robot Navigation and Embedded Applications

https://github.com/mt-upc/SegAugment

공식 코드

SegAugment: Maximizing the Utility of Speech Translation Data with Segmentation-based Augmentations

https://github.com/mt-upc/iwslt-2021

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End-to-End Speech Translation with Pre-trained Models and Adapters: UPC at IWSLT 2021

https://github.com/mt-upc/shas

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SHAS: Approaching optimal Segmentation for End-to-End Speech Translation

https://github.com/mtailanian/uflow

공식 코드

U-Flow: A U-shaped Normalizing Flow for Anomaly Detection with Unsupervised Threshold

https://github.com/mtec-tuhh/polypnextlstm

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PolypNextLSTM: A lightweight and fast polyp video segmentation network using ConvNext and ConvLSTM

https://github.com/mts-public/feature-characterization-for-profile-surface-texture

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Feature Characterization for Profile Surface Texture

https://github.com/mttr2021/MTTR

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End-to-End Referring Video Object Segmentation with Multimodal Transformers

https://github.com/mudit-adityaja/unsegmedgat

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UnSegMedGAT: Unsupervised Medical Image Segmentation using Graph Attention Networks Clustering

https://github.com/mueller-franzes/trabs

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Fibroglandular Tissue Segmentation in Breast MRI using Vision Transformers -- A multi-institutional evaluation

https://github.com/muhamadusman/assist

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Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion models

https://github.com/muhammad-ibraheem-siddiqui/persense

공식 코드

PerSense: Personalized Instance Segmentation in Dense Images

https://github.com/multimodallearning/dg-tta

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DG-TTA: Out-of-domain Medical Image Segmentation through Augmentation and Descriptor-driven Domain Generalization and Test-Time Adaptation

https://github.com/multimodallearning/samcarriestheburden

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SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation

https://github.com/multimodallearning/shape_matters

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Shape Matters: Detecting Vertebral Fractures Using Differentiable Point-Based Shape Decoding

https://github.com/mungomeng/deepmts

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DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CT

https://github.com/mungomeng/survival-adamss

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AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction from PET/CT Images

https://github.com/mungomeng/survival-deepmss

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AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction from PET/CT Images

https://github.com/mungomeng/survival-deepmts

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DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CT

https://github.com/mustafa1728/wlsegnet

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A Language-Guided Benchmark for Weakly Supervised Open Vocabulary Semantic Segmentation

https://github.com/mvaldenegro/marine-debris-fls-datasets

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The Marine Debris Dataset for Forward-Looking Sonar Semantic Segmentation

https://github.com/mvme-hbut/sgacnet

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Spatial-information Guided Adaptive Context-aware Network for Efficient RGB-D Semantic Segmentation

https://github.com/mwxinnn/unias

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Towards Accurate Unified Anomaly Segmentation

https://github.com/mwyburd/TEDS-Net

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TEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee Topology Preservation in Segmentations

https://github.com/naamiinepal/medvlsm

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

https://github.com/naamiinepal/synthetic-boost

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Synthetic Boost: Leveraging Synthetic Data for Enhanced Vision-Language Segmentation in Echocardiography

https://github.com/naamiinepal/tunevlseg

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TuneVLSeg: Prompt Tuning Benchmark for Vision-Language Segmentation Models

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