Artificial Intelligence in Colorectal Polyp Detection and Characterization

Le, Alexander; Salifu, Moro O.; McFarlane, Isabel M.

doi:10.15344/2456-8007/2021/157

Cited by 5 publications

(2 citation statements)

References 63 publications

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“…Currently, colonoscopy is the most common screening tool used in clinical diagnosis to detect abnormal polyps in the colon [6]. However, the high rate of misdiagnosis and high labour costs make colonoscopy ineffective in diagnosing early to mid-stage polyp lesions [6,7]. To effectively enhance patient outcomes, radiologists can improve diagnostic accuracy and efficiency through the use of computer-aided diagnostic procedures [8].…”

Section: Introductionmentioning

confidence: 99%

GCCSwin-UNet: Global Context and Cross-Shaped Windows Vision Transformer Network for Polyp Segmentation

Zhu

Chang

et al. 2023

Processes

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Accurate polyp segmentation is of great importance for the diagnosis and treatment of colon cancer. Convolutional neural networks (CNNs) have made significant strides in the processing of medical images in recent years. The limited structure of convolutional operations prevents CNNs from learning adequately about global and long-range semantic information interactions, despite the remarkable performance they have attained. Therefore, the GCCSwin-UNet framework is suggested in this study. Specifically, the model utilizes an encoder–decoder structure, using the patch-embedding layer for feature downsampling and the CSwin Transformer block as the encoder for contextual feature extraction. To restore the feature map’s spatial resolution during upsampling operations, a symmetric decoder and patch expansion layer are also created. In order to help the backbone module to do better feature learning, we also create a global context module (GCM) and a local position-enhanced module (LPEM). We conducted extensive experiments on the Kvasir-SEG and CVC-ClinicDB datasets, and compared them with existing methods. GCCSwin-UNet reached remarkable results with Dice and MIoU of 86.37% and 83.19% for Kvasir-SEG, respectively, and 91.26% and 84.65% for CVC-ClinicDB, respectively. Finally, quantitative analysis and statistical tests are applied to further demonstrate the validity and plausibility of our method.

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Section: Introductionmentioning

confidence: 99%

GCCSwin-UNet: Global Context and Cross-Shaped Windows Vision Transformer Network for Polyp Segmentation

Zhu

Chang

et al. 2023

Processes

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“…In the field of gastroenterology, these technologies can create an intelligent auxiliary system that can automatically detect and describe polyp information for a large number of videos and imaging data generated in the colorectal screening process, which helps to overcome the limitations of traditional colonoscopy and improve the quality of colonoscopy screening. Therefore, they make medical services intelligent in real sense and promote the prosperity and development of medical undertakings [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Polyp Detection from Colorectum Images by Using Attentive YOLOv5

Wan

Chen

2021

Diagnostics

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Background: High-quality colonoscopy is essential to prevent the occurrence of colorectal cancers. The data of colonoscopy are mainly stored in the form of images. Therefore, artificial intelligence-assisted colonoscopy based on medical images is not only a research hotspot, but also one of the effective auxiliary means to improve the detection rate of adenomas. This research has become the focus of medical institutions and scientific research departments and has important clinical and scientific research value. Methods: In this paper, we propose a YOLOv5 model based on a self-attention mechanism for polyp target detection. This method uses the idea of regression, using the entire image as the input of the network and directly returning the target frame of this position in multiple positions of the image. In the feature extraction process, an attention mechanism is added to enhance the contribution of information-rich feature channels and weaken the interference of useless channels; Results: The experimental results show that the method can accurately identify polyp images, especially for the small polyps and the polyps with inconspicuous contrasts, and the detection speed is greatly improved compared with the comparison algorithm. Conclusions: This study will be of great help in reducing the missed diagnosis of clinicians during endoscopy and treatment, and it is also of great significance to the development of clinicians’ clinical work.

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A Reliable and Sensitive Framework for Simultaneous Type and Stage Detection of Colorectal Cancer Polyps

et al. 2023

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This paper proposes a smart handheld textural sensing medical device with complementary Machine Learning (ML) algorithms to enable on-site Colorectal Cancer (CRC) polyp diagnosis and pathology of excised tumors. The proposed unique handheld edge device benefits from a unique tactile sensing module and a dual-stage machine learning algorithms (composed of a dilated residual network and a t-SNE engine) for polyp type and stiffness characterization. Solely utilizing the occlusion-free, illumination-resilient textural images captured by the proposed tactile sensor, the framework is able to sensitively and reliably identify the type and stage of CRC polyps by classifying their texture and stiffness, respectively. Moreover, the proposed handheld medical edge device benefits from internet connectivity for enabling remote digital pathology (boosting the diagnosis in operating rooms and promoting accessibility and equity in medical diagnosis).Recent literature reports that morphological characteristics of polyps (i.e., shape, size, and particularly texture) can aid clinicians with early diagnosis and classifications of cancer types and stages, treatment outcomes, and, more importantly, survival of cancer patients [4]. Despite their impactful attributes, decision-making based on morphological characteristics can depend on the clinician and be

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Artificial Intelligence in Colorectal Polyp Detection and Characterization

Cited by 5 publications

References 63 publications

GCCSwin-UNet: Global Context and Cross-Shaped Windows Vision Transformer Network for Polyp Segmentation

GCCSwin-UNet: Global Context and Cross-Shaped Windows Vision Transformer Network for Polyp Segmentation

Polyp Detection from Colorectum Images by Using Attentive YOLOv5

A Reliable and Sensitive Framework for Simultaneous Type and Stage Detection of Colorectal Cancer Polyps

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