# TGANet: Text-guided attention for improved polyp segmentation

arXiv: https://arxiv.org/abs/2205.04280 · HTML: https://mlsanity.com/paper/2205.04280/

## Key facts

- Cited by 59 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 19 references that are in the map.
- First posted on arXiv 9 May 2022.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Kvasir-SEG: A Segmented Polyp Dataset](https://mlsanity.com/paper/1911.07069/) (2019)
- [PraNet: Parallel Reverse Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2006.11392/) (2020)
- [HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPS](https://mlsanity.com/paper/2101.07172/) (2021)
- [MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation](https://mlsanity.com/paper/2105.07451/) (2021)
- [BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages](https://mlsanity.com/paper/1710.02187/) (2017)
- [NeoUNet: Towards accurate colon polyp segmentation and neoplasm detection](https://mlsanity.com/paper/2107.05023/) (2021)
- [A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation](https://mlsanity.com/paper/2107.12435/) (2021)

## Built on by (most cited first, 30 of 59)

- [LViT: Language meets Vision Transformer in Medical Image Segmentation](https://mlsanity.com/paper/2206.14718/) (2022)
- [Exploring Transfer Learning in Medical Image Segmentation using Vision-Language Models](https://mlsanity.com/paper/2308.07706/) (2023)
- [Cross-Modal Conditioned Reconstruction for Language-guided Medical Image Segmentation](https://mlsanity.com/paper/2404.02845/) (2024)
- [WeakPolyp: You Only Look Bounding Box for Polyp Segmentation](https://mlsanity.com/paper/2307.10912/) (2023)
- [TransResU-Net: Transformer based ResU-Net for Real-Time Colonoscopy Polyp Segmentation](https://mlsanity.com/paper/2206.08985/) (2022)
- [CausalCLIPSeg: Unlocking CLIP's Potential in Referring Medical Image Segmentation with Causal Intervention](https://mlsanity.com/paper/2503.15949/) (2025)
- [Frontiers in Intelligent Colonoscopy](https://mlsanity.com/paper/2410.17241/) (2024)
- [A Survey on Deep Learning for Polyp Segmentation: Techniques, Challenges and Future Trends](https://mlsanity.com/paper/2311.18373/) (2023)
- [ProMamba: Prompt-Mamba for polyp segmentation](https://mlsanity.com/paper/2403.13660/) (2024)
- [EndoOmni: Zero-Shot Cross-Dataset Depth Estimation in Endoscopy by Robust Self-Learning from Noisy Labels](https://mlsanity.com/paper/2409.05442/) (2024)
- [Bi-VLGM : Bi-Level Class-Severity-Aware Vision-Language Graph Matching for Text Guided Medical Image Segmentation](https://mlsanity.com/paper/2305.12231/) (2023)
- [Concealed Object Segmentation with Hierarchical Coherence Modeling](https://mlsanity.com/paper/2401.11767/) (2024)
- [DuSSS: Dual Semantic Similarity-Supervised Vision-Language Model for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2412.12492/) (2024)
- [Coarse-to-Fine Covid-19 Segmentation via Vision-Language Alignment](https://mlsanity.com/paper/2303.00279/) (2023)
- [PolypDB: A Curated Multi-Center Dataset for Development of AI Algorithms in Colonoscopy](https://mlsanity.com/paper/2409.00045/) (2024)
- [XBound-Former: Toward Cross-scale Boundary Modeling in Transformers](https://mlsanity.com/paper/2206.00806/) (2022)
- [MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training](https://mlsanity.com/paper/2411.15576/) (2024)
- [Language-guided Medical Image Segmentation with Target-informed Multi-level Contrastive Alignments](https://mlsanity.com/paper/2412.13533/) (2024)
- [STPNet: Scale-aware Text Prompt Network for Medical Image Segmentation](https://mlsanity.com/paper/2504.01561/) (2025)
- [Colorectal Polyp Segmentation in the Deep Learning Era: A Comprehensive Survey](https://mlsanity.com/paper/2401.11734/) (2024)
- [MNet-SAt: A Multiscale Network with Spatial-enhanced Attention for Segmentation of Polyps in Colonoscopy](https://mlsanity.com/paper/2412.19464/) (2024)
- [Uncertainty-Aware Vision-Language Segmentation for Medical Imaging](https://mlsanity.com/paper/2602.14498/) (2026)
- [Edge-aware Feature Aggregation Network for Polyp Segmentation](https://mlsanity.com/paper/2309.10523/) (2023)
- [MAST: Video Polyp Segmentation with a Mixture-Attention Siamese Transformer](https://mlsanity.com/paper/2401.12439/) (2024)
- [LGPS: A Lightweight GAN-Based Approach for Polyp Segmentation in Colonoscopy Images](https://mlsanity.com/paper/2503.18294/) (2025)
- [PSTNet: Enhanced Polyp Segmentation with Multi-scale Alignment and Frequency Domain Integration](https://mlsanity.com/paper/2409.08501/) (2024)
- [TransRUPNet for Improved Polyp Segmentation](https://mlsanity.com/paper/2306.02176/) (2023)
- [RetSeg: Retention-based Colorectal Polyps Segmentation Network](https://mlsanity.com/paper/2310.05446/) (2023)
- [Optimizing Universal Lesion Segmentation: State Space Model-Guided Hierarchical Networks with Feature Importance Adjustment](https://mlsanity.com/paper/2404.17235/) (2024)
- [EPPS: Advanced Polyp Segmentation via Edge Information Injection and Selective Feature Decoupling](https://mlsanity.com/paper/2405.11846/) (2024)

## Abstract (arXiv)

Colonoscopy is a gold standard procedure but is highly operator-dependent. Automated polyp segmentation, a precancerous precursor, can minimize missed rates and timely treatment of colon cancer at an early stage. Even though there are deep learning methods developed for this task, variability in polyp size can impact model training, thereby limiting it to the size attribute of the majority of samples in the training dataset that may provide sub-optimal results to differently sized polyps. In this work, we exploit size-related and polyp number-related features in the form of text attention during training. We introduce an auxiliary classification task to weight the text-based embedding that allows network to learn additional feature representations that can distinctly adapt to differently sized polyps and can adapt to cases with multiple polyps. Our experimental results demonstrate that these added text embeddings improve the overall performance of the model compared to state-of-the-art segmentation methods. We explore four different datasets and provide insights for size-specific improvements. Our proposed text-guided attention network (TGANet) can generalize well to variable-sized polyps in different datasets.