Papers with Code 아카이브

Surgical phase recognition

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크: 2
평가표: 4
보존된 평가 기록: 16

The first 40 videos are used for training, the last 40 videos are used for testing.

코드

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

https://github.com/BCV-Uniandes/MuST

공식 코드

MuST: Multi-Scale Transformers for Surgical Phase Recognition

https://github.com/ChinaYi/casual_tcn

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Not End-to-End: Explore Multi-Stage Architecture for Online Surgical Phase Recognition

https://github.com/MRUIL/LoViT

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LoViT: Long Video Transformer for Surgical Phase Recognition

https://github.com/Negin-Ghamsarian/AdaptNet-MICCAI2021

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LensID: A CNN-RNN-Based Framework Towards Lens Irregularity Detection in Cataract Surgery Videos

https://github.com/YuemingJin/MTRCNet-CL

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Multi-Task Recurrent Convolutional Network with Correlation Loss for Surgical Video Analysis

https://github.com/bcv-uniandes/grasp

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Pixel-Wise Recognition for Holistic Surgical Scene Understanding

https://github.com/bcv-uniandes/matis

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Pixel-Wise Recognition for Holistic Surgical Scene Understanding

https://github.com/bcv-uniandes/tapir

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Towards Holistic Surgical Scene Understanding

https://github.com/camma-public/selfsupsurg

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Dissecting Self-Supervised Learning Methods for Surgical Computer Vision

https://github.com/camma-public/surglatentgraph

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Encoding Surgical Videos as Latent Spatiotemporal Graphs for Object and Anatomy-Driven Reasoning

https://github.com/fujiry0/egosurgery

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EgoSurgery-Phase: A Dataset of Surgical Phase Recognition from Egocentric Open Surgery Videos

https://github.com/isyangshu/surgformer

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Surgformer: Surgical Transformer with Hierarchical Temporal Attention for Surgical Phase Recognition

https://github.com/kk42yy/dacat

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DACAT: Dual-stream Adaptive Clip-aware Time Modeling for Robust Online Surgical Phase Recognition

https://github.com/lxj22/surgplan-plus

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SurgPLAN++: Universal Surgical Phase Localization Network for Online and Offline Inference

https://github.com/rcao-hk/sr-mamba

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SR-Mamba: Effective Surgical Phase Recognition with State Space Model

https://github.com/royhirsch/endossl

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Self-Supervised Learning for Endoscopic Video Analysis

https://github.com/sahar-nasiri/dist

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Dual Invariance Self-training for Reliable Semi-supervised Surgical Phase Recognition

https://github.com/tiancuteqy/endomamba

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EndoMamba: An Efficient Foundation Model for Endoscopic Videos via Hierarchical Pre-training

https://github.com/tobiascz/TeCNO

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TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks

https://github.com/visurg-ai/surg-3m

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Surg-3M: A Dataset and Foundation Model for Perception in Surgical Settings

https://github.com/xjgaocs/Trans-SVNet

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Trans-SVNet: Accurate Phase Recognition from Surgical Videos via Hybrid Embedding Aggregation Transformer

https://github.com/xmed-lab/distillingself

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Free Lunch for Surgical Video Understanding by Distilling Self-Supervisions

https://github.com/xmed-lab/sahc

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Exploring Segment-level Semantics for Online Phase Recognition from Surgical Videos

https://github.com/xmed-lab/timestamp-surgical

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Less is More: Surgical Phase Recognition from Timestamp Supervision

https://github.com/zxnyyyyy/sprmamba

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SPRMamba: Surgical Phase Recognition for Endoscopic Submucosal Dissection with Mamba

https://gitlab.com/nct_tso_public/phasemetrics

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Metrics Matter in Surgical Phase Recognition

https://gitlab.com/nct_tso_public/pretrain_tc

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Temporal coherence-based self-supervised learning for laparoscopic workflow analysis

https://gitlab.com/nct_tso_public/tunes

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TUNeS: A Temporal U-Net with Self-Attention for Video-based Surgical Phase Recognition

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