Colonoscopy Versus Fecal Immunochemical Test for Reducing Colorectal Cancer Risk: A Population-Based Case-Control Study

PolypMixNet: Enhancing semi-supervised polyp segmentation with polyp-aware augmentation

BACKGROUND

AI-assisted polyp segmentation in colonoscopy plays a crucial role in enabling prompt diagnosis and treatment of colorectal cancer. However, the lack of sufficient annotated data poses a significant challenge for supervised learning approaches. Existing semi-supervised learning methods also suffer from performance degradation, mainly due to task-specific characteristics, such as class imbalance in polyp segmentation.

PURPOSE

The purpose of this work is to develop an effective semi-supervised learning framework for accurate polyp segmentation in colonoscopy, addressing limited annotated data and class imbalance challenges.

METHODS

We proposed PolypMixNet, a semi-supervised framework, for colorectal polyp segmentation, utilizing novel augmentation techniques and a Mean Teacher architecture to improve model performance. PolypMixNet introduces the polyp-aware mixup (PolypMix) algorithm and incorporates dual-level consistency regularization. PolypMix addresses the class imbalance in colonoscopy datasets and enhances the diversity of training data. By performing a polyp-aware mixup on unlabeled samples, it generates mixed images with polyp context along with their artificial labels. A polyp-directed soft pseudo-labeling (PDSPL) mechanism was proposed to generate high-quality pseudo labels and eliminate the dilution of lesion features caused by mixup operations. To ensure consistency in the training phase, we introduce the PolypMix prediction consistency (PMPC) loss and PolypMix attention consistency (PMAC) loss, enforcing consistency at both image and feature levels. Code is available at https://github.com/YChienHung/PolypMix.

RESULTS

PolypMixNet was evaluated on four public colonoscopy datasets, achieving 88.97% Dice and 88.85% mIoU on the benchmark dataset of Kvasir-SEG. In scenarios where the labeled training data is limited to 15%, PolypMixNet outperforms the state-of-the-art semi-supervised approaches with a 2.88-point improvement in Dice. It also shows the ability to reach performance comparable to the fully supervised counterpart. Additionally, we conducted extensive ablation studies to validate the effectiveness of each module and highlight the superiority of our proposed approach.

CONCLUSION

PolypMixNet effectively addresses the challenges posed by limited annotated data and unbalanced class distributions in polyp segmentation. By leveraging unlabeled data and incorporating novel augmentation and consistency regularization techniques, our method achieves state-of-the-art performance. We believe that the insights and contributions presented in this work will pave the way for further advancements in semi-supervised polyp segmentation and inspire future research in the medical imaging domain.

Faecal immunochemical testing (FIT) in patients with signs or symptoms of suspected colorectal cancer (CRC): a joint guideline from the Association of Coloproctology of Great Britain and Ireland (ACPGBI) and the British Society of Gastroenterology (BSG)

Faecal immunochemical testing (FIT) has a high sensitivity for the detection of colorectal cancer (CRC). In a symptomatic population FIT may identify those patients who require colorectal investigation with the highest priority. FIT offers considerable advantages over the use of symptoms alone, as an objective measure of risk with a vastly superior positive predictive value for CRC, while conversely identifying a truly low risk cohort of patients. The aim of this guideline was to provide a clear strategy for the use of FIT in the diagnostic pathway of people with signs or symptoms of a suspected diagnosis of CRC. The guideline was jointly developed by the Association of Coloproctology of Great Britain and Ireland/British Society of Gastroenterology, specifically by a 21-member multidisciplinary guideline development group (GDG). A systematic review of 13 535 publications was undertaken to develop 23 evidence and expert opinion-based recommendations for the triage of people with symptoms of a suspected CRC diagnosis in primary care. In order to achieve consensus among a broad group of key stakeholders, we completed an extended Delphi of the GDG, and also 61 other individuals across the UK and Ireland, including by members of the public, charities and primary and secondary care. Seventeen research recommendations were also prioritised to inform clinical management.