Multiscale Hybrid Attention Network for Automated Segmentation of Colorectal Polyps

Öz

Accurate and reliable segmentation of colorectal polyps plays a critical role in the early diagnosis of colorectal cancer. Although deep learning–based methods have shown significant progress in recent years, the variations in polyp size, shape, color, and texture can adversely affect segmentation performance. In this study, a hybrid network model is proposed for automatic polyp segmentation. The proposed approach utilizes multi-level single-layer feature maps extracted from a DAF3D-based structure, which has demonstrated strong performance in deep feature extraction. These feature maps are first processed with a channel attention module and then with a reverse attention module to generate a multi-layer feature representation. While the channel attention module emphasizes the most informative channels, the reverse attention module enhances boundary regions, thereby improving segmentation accuracy. To evaluate the effect of these modules, scenarios using only channel attention, only reverse attention, and both modules together were compared, revealing that the integrated use of both modules achieved the highest performance. Additionally, the proposed model was compared with several polyp segmentation networks from the literature under the same backbone architecture and identical training conditions. Experiments conducted on multiple datasets demonstrate that the hybrid architecture provides significantly superior performance in mean Dice and mean IoU metrics compared to other methods. The findings indicate that the proposed approach is an effective, stable, and highly accurate method for polyp segmentation.

Anahtar Kelimeler

• Polyp segmentation
• Attention
• Chanal attention modüle
• Reverse attention modüle

Etik Beyan

This study is a research paper that does not include a survey and the datasets used in the implementation phase were obtained from publicly accessible websites; therefore, it does not require ethical committee approval.

Kolorektal Poliplerin Otomatik Segmentasyonu için Çok Ölçekli Hibrit Dikkat Ağı

Öz

Kolorektal poliplerin doğru ve güvenilir bir şekilde bölütlenmesi, kolorektal kanserin erken tanısında kritik bir öneme sahiptir. Son yıllarda derin öğrenme tabanlı yöntemlerle önemli ilerlemeler kaydedilmiş olsa da poliplerin boyut, şekil, renk ve doku açısından gösterdiği çeşitlilik, segmentasyon başarımını olumsuz etkileyebilmektedir. Bu çalışmada, otomatik polip segmentasyonu için hibrit bir ağ modeli önerilmektedir. Önerilen yaklaşımda, derin özellik çıkarımında başarılı olduğu bilinen DAF3D tabanlı yapıda elde edilen çok seviyeli tek katmanlı özellik haritaları kullanılmaktadır. Bu özellik haritaları önce kanal dikkat modülü, ardından ters dikkat modülü ile işlenerek çok katmanlı bir özellik temsiline dönüştürülmektedir. Kanal dikkat modülü en bilgilendirici kanalları öne çıkarırken, ters dikkat modülü sınır bölgelerini belirginleştirerek segmentasyon doğruluğunu artırmaktadır. Modüllerin etkisini değerlendirmek amacıyla yalnızca kanal dikkat, yalnızca ters dikkat ve her iki modülün birlikte kullanıldığı senaryolar karşılaştırılmış; her iki modülün birlikte kullanıldığı durumda en yüksek başarımın elde edildiği görülmüştür. Ayrıca önerilen model, literatürde yer alan çeşitli polip segmentasyon ağlarıyla aynı omurga mimarisi ve eşit eğitim koşulları altında karşılaştırılmıştır. Birden fazla veri seti üzerinde gerçekleştirilen deneyler, hibrit yapının mean Dice ve mean IoU metriklerinde diğer yöntemlere kıyasla anlamlı derecede üstün performans gösterdiğini ortaya koymaktadır. Elde edilen bulgular, önerilen yaklaşımın polip segmentasyonu için etkili, kararlı ve yüksek doğruluk sağlayan bir yöntem olduğunu göstermektedir.

Anahtar Kelimeler

• Polip segmentasyonu
• Dikkat
• Kanal dikkat modülü
• Ters dikkat modülü

Etik Beyan

Bu çalışma, anket çalışması içermeyen ve uygulama aşamasında kullanılan veri setleri herkes tarafından erişilebilir sitelerden alınmış bir araştırma makalesi niteliğinde olup, etik kurul onayı gerektirmemektedir.

Kaynakça

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