# Holistic White-light Polyp Classification via Alignment-free Dense Distillation of Auxiliary Optical Chromoendoscopy

arXiv: https://arxiv.org/abs/2505.19319 · HTML: https://mlsanity.com/paper/2505.19319/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 25 May 2025.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Class Re-Activation Maps for Weakly-Supervised Semantic Segmentation](https://mlsanity.com/paper/2203.00962/) (2022)
- [Token Contrast for Weakly-Supervised Semantic Segmentation](https://mlsanity.com/paper/2303.01267/) (2023)
- [SALI: Short-term Alignment and Long-term Interaction Network for Colonoscopy Video Polyp Segmentation](https://mlsanity.com/paper/2406.13532/) (2024)
- [FFCNet: Fourier Transform-Based Frequency Learning and Complex Convolutional Network for Colon Disease Classification](https://mlsanity.com/paper/2207.01287/) (2022)
- [SSL-CPCD: Self-supervised learning with composite pretext-class discrimination for improved generalisability in endoscopic image analysis](https://mlsanity.com/paper/2306.00197/) (2023)
- [Colorectal Polyp Classification from White-light Colonoscopy Images via Domain Alignment](https://mlsanity.com/paper/2108.02476/) (2021)
- [Toward Clinically Assisted Colorectal Polyp Recognition via Structured Cross-modal Representation Consistency](https://mlsanity.com/paper/2206.11826/) (2022)
- [MonoBox: Tightness-free Box-supervised Polyp Segmentation using Monotonicity Constraint](https://mlsanity.com/paper/2404.01188/) (2024)

## Built on by (most cited first, 4 of 4)

- [Pairing-free Group-level Knowledge Distillation for Robust Gastrointestinal Lesion Classification in White-Light Endoscopy](https://mlsanity.com/paper/2601.09209/) (2026)
- [Polyp-D2ATL: Deep Domain-Adaptive Transfer Learning for Colorectal Polyp Classification under Label Distribution Shift](https://mlsanity.com/paper/2606.15000/) (2026)
- [Registration-Grounded Spectral Fusion for Unregistered WLI/NBI Endoscopic Lesion Segmentation](https://mlsanity.com/paper/2607.26395/) (2026)
- [Linguistically-Aligned and Visually-Grounded Preference Optimization for Clinically-Augmented Medical Report Generation](https://mlsanity.com/paper/2608.08494/) (2026)

## Abstract (arXiv)

White Light Imaging (WLI) and Narrow Band Imaging (NBI) are the two main colonoscopic modalities for polyp classification. While NBI, as optical chromoendoscopy, offers valuable vascular details, WLI remains the most common and often the only available modality in resource-limited settings. However, WLI-based methods typically underperform, limiting their clinical applicability. Existing approaches transfer knowledge from NBI to WLI through global feature alignment but often rely on cropped lesion regions, which are susceptible to detection errors and neglect contextual and subtle diagnostic cues. To address this, this paper proposes a novel holistic classification framework that leverages full-image diagnosis without requiring polyp localization. The key innovation lies in the Alignment-free Dense Distillation (ADD) module, which enables fine-grained cross-domain knowledge distillation regardless of misalignment between WLI and NBI images. Without resorting to explicit image alignment, ADD learns pixel-wise cross-domain affinities to establish correspondences between feature maps, guiding the distillation along the most relevant pixel connections. To further enhance distillation reliability, ADD incorporates Class Activation Mapping (CAM) to filter cross-domain affinities, ensuring the distillation path connects only those semantically consistent regions with equal contributions to polyp diagnosis. Extensive results on public and in-house datasets show that our method achieves state-of-the-art performance, relatively outperforming the other approaches by at least 2.5% and 16.2% in AUC, respectively. Code is available at: https://github.com/Huster-Hq/ADD.