Induce to Empower: Improving Lightweight Baselines via Foundation Model Induction for Generalized Polyp Segmentation

• URL: http://arxiv.org/abs/2607.17208v1
• Date: Sun, 19 Jul 2026 11:57:19 GMT
• Title: Induce to Empower: Improving Lightweight Baselines via Foundation Model Induction for Generalized Polyp Segmentation
• Abstract summary: We propose Lite-Polyp Inductor (Lite-Pi), a novel foundation model induction framework that significantly enhances lightweight polyp segmentation baselines.<n>Our proposed framework generates FM-specific prototype representations and aligns them semantically with the corresponding foundation model priors.<n>Experiments across five polyp segmentation benchmark demonstrate that Lite- significantly improves lightweight baselines.
• Score: 17.181959776318823
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated polyp segmentation in colonoscopy continues to pose challenges due to substantial appearance variations and indistinct polyp boundaries. Although emerging foundation models (FMs) such as DINOv2, SAM, and OneFormer, demonstrate remarkable generalization capabilities, their direct transfer to the polyp segmentation task and deployment in real-time clinical settings are difficult due to lack of large-scale labeled data and high computational demands. In addition, adopting multiple FMs together raises concerns, even though they encode complementary semantic and structural information. While lightweight models, including U-Net, PraNet and U-Net++, are computationally efficient, they often struggle to generalize across datasets due to limited representational capacity. To address this gap, we propose Lite-Polyp Inductor (Lite-Pi), a novel foundation model induction framework that significantly enhances lightweight polyp segmentation baselines. Our proposed framework generates FM-specific prototype representations and aligns them semantically with the corresponding foundation model priors through reconstruction-based supervision. Subsequently, transformer-based fusion is introduced to highlight the polyp relevant representations, including salient boundary information, while preserving complementary semantic cues. Extensive experiments across five polyp segmentation benchmark datasets demonstrate that Lite-π significantly improves lightweight baselines, achieving superior generalization performance with minimal computational overhead and thereby, offering a practical solution for generalized polyp segmentation. Our code is available at GitHub. https://github.com/lostinrepo/Lite-Pi

Related papers

• Sharpening Lightweight Models for Generalized Polyp Segmentation: A Boundary Guided Distillation from Foundation Models [5.836991649815995]
  polyp segmentation is critical for early colorectal cancer detection and its prevention, yet remains challenging due to weak boundaries, large appearance variations, and limited annotated data.<n>We propose LiteBounD, a framework that transfers semantic and structural priors from multiple Vision Foundation Models into compact segmentation backbones.<n>LiteBounD consistently outperforms its lightweight baselines by a significant margin and performance competitive with state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-04-20T06:25:06Z)
• ASGNet: Adaptive Spectrum Guidance Network for Automatic Polyp Segmentation [31.86667278394464]
  Early identification and removal of polyps can reduce the risk of developing colorectal cancer.<n>The diverse morphologies, complex backgrounds and often concealed nature of polyps make polyp segmentation in colonoscopy images highly challenging.<n>We propose a novel adaptive spectrum guidance network, called ASGNet, which addresses the limitations of spatial perception.
  arXiv  Detail & Related papers  (2026-04-16T08:10:20Z)
• From SAM to DINOv2: Towards Distilling Foundation Models to Lightweight Baselines for Generalized Polyp Segmentation [9.452022523459886]
  Polyp-DiFoM is a framework that transfers the rich representations of foundation models into lightweight segmentation baselines.<n>It consistently outperforms baseline models significantly, as well as the state-of-the-art model, with nearly 9 times reduced overhead.
  arXiv  Detail & Related papers  (2025-12-10T04:25:01Z)
• Polyp-SES: Automatic Polyp Segmentation with Self-Enriched Semantic Model [7.424888086388194]
  We propose an innovative method named Automatic Polyp with Self-Enriched Semantic Model'' to address limitations in existing approaches. First, we extract a sequence of features from an input image and decode high-level features to generate an initial segmentation mask. Using the proposed self-enriched semantic module, we query potential semantics and augment deep features with additional semantics, thereby aiding the model in understanding context more effectively.
  arXiv  Detail & Related papers  (2024-10-02T03:34:23Z)
• ASPS: Augmented Segment Anything Model for Polyp Segmentation [77.25557224490075]
  The Segment Anything Model (SAM) has introduced unprecedented potential for polyp segmentation. SAM's Transformer-based structure prioritizes global and low-frequency information. CFA integrates a trainable CNN encoder branch with a frozen ViT encoder, enabling the integration of domain-specific knowledge.
  arXiv  Detail & Related papers  (2024-06-30T14:55:32Z)
• ECC-PolypDet: Enhanced CenterNet with Contrastive Learning for Automatic Polyp Detection [88.4359020192429]
  Existing methods either involve computationally expensive context aggregation or lack prior modeling of polyps, resulting in poor performance in challenging cases. In this paper, we propose the Enhanced CenterNet with Contrastive Learning (ECC-PolypDet), a two-stage training & end-to-end inference framework. Box-assisted Contrastive Learning (BCL) during training to minimize the intra-class difference and maximize the inter-class difference between foreground polyps and backgrounds, enabling our model to capture concealed polyps. In the fine-tuning stage, we introduce the IoU-guided Sample Re-weighting
  arXiv  Detail & Related papers  (2024-01-10T07:03:41Z)
• Lesion-aware Dynamic Kernel for Polyp Segmentation [49.63274623103663]
  We propose a lesion-aware dynamic network (LDNet) for polyp segmentation. It is a traditional u-shape encoder-decoder structure incorporated with a dynamic kernel generation and updating scheme. This simple but effective scheme endows our model with powerful segmentation performance and generalization capability.
  arXiv  Detail & Related papers  (2023-01-12T09:53:57Z)
• BoxPolyp:Boost Generalized Polyp Segmentation Using Extra Coarse Bounding Box Annotations [79.17754846553866]
  We propose a boosted BoxPolyp model to make full use of both accurate mask and extra coarse box annotations. In practice, box annotations are applied to alleviate the over-fitting issue of previous polyp segmentation models. Our proposed model outperforms previous state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2022-12-07T07:45:50Z)
• Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers [124.01928050651466]
  We propose a new type of polyp segmentation method, named Polyp-PVT. The proposed model, named Polyp-PVT, effectively suppresses noises in the features and significantly improves their expressive capabilities.
  arXiv  Detail & Related papers  (2021-08-16T07:09:06Z)
• PraNet: Parallel Reverse Attention Network for Polyp Segmentation [155.93344756264824]
  We propose a parallel reverse attention network (PraNet) for accurate polyp segmentation in colonoscopy images. We first aggregate the features in high-level layers using a parallel partial decoder (PPD) In addition, we mine the boundary cues using a reverse attention (RA) module, which is able to establish the relationship between areas and boundary cues.
  arXiv  Detail & Related papers  (2020-06-13T08:13:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.