The paper "PraNet: Parallel Reverse Attention Network for Polyp Segmentation" introduces a novel deep neural network designed for accurate and real-time polyp segmentation in colonoscopy images. The main challenges in polyp segmentation include the diversity of polyp sizes, colors, and textures, as well as the blurred boundaries between polyps and surrounding mucosa. To address these issues, the authors propose the Parallel Reverse Attention Network ($PraNet$). $PraNet$ consists of two key components: a parallel partial decoder (PPD) and a reverse attention (RA) module. The PPD aggregates high-level features to generate a global map, which serves as an initial guidance area for the RA module. The RA module mines boundary cues by establishing the relationship between areas and boundaries, enhancing the accuracy of the segmentation. The recurrent cooperation between areas and boundaries calibrates misaligned predictions, improving overall segmentation performance. The paper evaluates $PraNet$ on five challenging datasets using various metrics, demonstrating significant improvements over existing state-of-the-art (SOTA) models. $PraNet$ achieves high accuracy (mean Dice = 0.898 on the Kvasir dataset) without any pre- or post-processing, and it runs at a real-time speed of ~50 fps. The authors also conduct ablation studies to validate the effectiveness of each component of $PraNet$. In conclusion, $PraNet$ offers a robust and flexible architecture that can be further enhanced with additional modules, making it a valuable tool for polyp segmentation in colonoscopy images.The paper "PraNet: Parallel Reverse Attention Network for Polyp Segmentation" introduces a novel deep neural network designed for accurate and real-time polyp segmentation in colonoscopy images. The main challenges in polyp segmentation include the diversity of polyp sizes, colors, and textures, as well as the blurred boundaries between polyps and surrounding mucosa. To address these issues, the authors propose the Parallel Reverse Attention Network ($PraNet$). $PraNet$ consists of two key components: a parallel partial decoder (PPD) and a reverse attention (RA) module. The PPD aggregates high-level features to generate a global map, which serves as an initial guidance area for the RA module. The RA module mines boundary cues by establishing the relationship between areas and boundaries, enhancing the accuracy of the segmentation. The recurrent cooperation between areas and boundaries calibrates misaligned predictions, improving overall segmentation performance. The paper evaluates $PraNet$ on five challenging datasets using various metrics, demonstrating significant improvements over existing state-of-the-art (SOTA) models. $PraNet$ achieves high accuracy (mean Dice = 0.898 on the Kvasir dataset) without any pre- or post-processing, and it runs at a real-time speed of ~50 fps. The authors also conduct ablation studies to validate the effectiveness of each component of $PraNet$. In conclusion, $PraNet$ offers a robust and flexible architecture that can be further enhanced with additional modules, making it a valuable tool for polyp segmentation in colonoscopy images.