# Holistic Surgical Phase Recognition with Hierarchical Input Dependent State Space Models

arXiv: https://arxiv.org/abs/2506.21330 · HTML: https://mlsanity.com/paper/2506.21330/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 35 references that are in the map.
- First posted on arXiv 26 Jun 2025.

## Builds on (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality](https://mlsanity.com/paper/2405.21060/) (2024)
- [Simplified State Space Layers for Sequence Modeling](https://mlsanity.com/paper/2208.04933/) (2022)
- [Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers](https://mlsanity.com/paper/2110.13985/) (2021)
- [HiPPO: Recurrent Memory with Optimal Polynomial Projections](https://mlsanity.com/paper/2008.07669/) (2020)
- [Hungry Hungry Hippos: Towards Language Modeling with State Space Models](https://mlsanity.com/paper/2212.14052/) (2022)
- [On the Parameterization and Initialization of Diagonal State Space Models](https://mlsanity.com/paper/2206.11893/) (2022)
- [MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation](https://mlsanity.com/paper/1903.01945/) (2019)
- [EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos](https://mlsanity.com/paper/1602.03012/) (2016)
- [Liquid Time-constant Networks](https://mlsanity.com/paper/2006.04439/) (2020)
- [Liquid Structural State-Space Models](https://mlsanity.com/paper/2209.12951/) (2022)
- [Trans-SVNet: Accurate Phase Recognition from Surgical Videos via Hybrid Embedding Aggregation Transformer](https://mlsanity.com/paper/2103.09712/) (2021)
- [Multi-Task Recurrent Convolutional Network with Correlation Loss for Surgical Video Analysis](https://mlsanity.com/paper/1907.06099/) (2019)
- [Temporal Memory Relation Network for Workflow Recognition from Surgical Video](https://mlsanity.com/paper/2103.16327/) (2021)
- [Comparative Validation of Machine Learning Algorithms for Surgical Workflow and Skill Analysis with the HeiChole Benchmark](https://mlsanity.com/paper/2109.14956/) (2021)
- [LoViT: Long Video Transformer for Surgical Phase Recognition](https://mlsanity.com/paper/2305.08989/) (2023)
- [Pixel-Wise Recognition for Holistic Surgical Scene Understanding](https://mlsanity.com/paper/2401.11174/) (2024)
- [Hydra: Bidirectional State Space Models Through Generalized Matrix Mixers](https://mlsanity.com/paper/2407.09941/) (2024)
- [Multi-Task Temporal Convolutional Networks for Joint Recognition of Surgical Phases and Steps in Gastric Bypass Procedures](https://mlsanity.com/paper/2102.12218/) (2021)
- [The TUM LapChole dataset for the M2CAI 2016 workflow challenge](https://mlsanity.com/paper/1610.09278/) (2016)
- [Surgformer: Surgical Transformer with Hierarchical Temporal Attention for Surgical Phase Recognition](https://mlsanity.com/paper/2408.03867/) (2024)
- [Not End-to-End: Explore Multi-Stage Architecture for Online Surgical Phase Recognition](https://mlsanity.com/paper/2107.04810/) (2021)
- [MuST: Multi-Scale Transformers for Surgical Phase Recognition](https://mlsanity.com/paper/2407.17361/) (2024)
- [SR-Mamba: Effective Surgical Phase Recognition with State Space Model](https://mlsanity.com/paper/2407.08333/) (2024)

## Built on by (most cited first, 3 of 3)

- [DSTED: Decoupling Temporal Stabilization and Discriminative Enhancement for Surgical Workflow Recognition](https://mlsanity.com/paper/2512.19387/) (2025)
- [Stabilizing Temporal Inference Dynamics for Online Surgical Phase Recognition](https://mlsanity.com/paper/2605.16387/) (2026)
- [LaSeD: Label-Semantic Self-Distillation for Visual-Only Surgical Phase Recognition](https://mlsanity.com/paper/2609.18971/) (2026)

## Abstract (arXiv)

Surgical workflow analysis is essential in robot-assisted surgeries, yet the long duration of such procedures poses significant challenges for comprehensive video analysis. Recent approaches have predominantly relied on transformer models; however, their quadratic attention mechanism restricts efficient processing of lengthy surgical videos. In this paper, we propose a novel hierarchical input-dependent state space model that leverages the linear scaling property of state space models to enable decision making on full-length videos while capturing both local and global dynamics. Our framework incorporates a temporally consistent visual feature extractor, which appends a state space model head to a visual feature extractor to propagate temporal information. The proposed model consists of two key modules: a local-aggregation state space model block that effectively captures intricate local dynamics, and a global-relation state space model block that models temporal dependencies across the entire video. The model is trained using a hybrid discrete-continuous supervision strategy, where both signals of discrete phase labels and continuous phase progresses are propagated through the network. Experiments have shown that our method outperforms the current state-of-the-art methods by a large margin (+2.8% on Cholec80, +4.3% on MICCAI2016, and +12.9% on Heichole datasets). Code will be publicly available after paper acceptance.