Classification Of Polyps Research Articles

Advancements in the use of AI in the diagnosis and management of inflammatory bowel disease.

Inflammatory bowel disease (IBD) causes chronic inflammation of the colon and digestive tract, and it can be classified as Crohn's disease (CD) and Ulcerative colitis (UC). IBD is more prevalent in Europe and North America, however, since the beginning of the 21st century it has been increasing in South America, Asia, and Africa, leading to its consideration as a worldwide problem. Optical colonoscopy is one of the crucial tests in diagnosing and assessing the progression and prognosis of IBD, as it allows a real-time optical visualization of the colonic wall and ileum and allows for the collection of tissue samples. The accuracy of colonoscopy procedures depends on the expertise and ability of the endoscopists. Therefore, algorithms based on Deep Learning (DL) and Convolutional Neural Networks (CNN) for colonoscopy images and videos are growing in popularity, especially for the detection and classification of colorectal polyps. The performance of this system is dependent on the quality and quantity of the data used for training. There are several datasets publicly available for endoscopy images and videos, but most of them are solely specialized in polyps. The use of DL algorithms to detect IBD is still in its inception, most studies are based on assessing the severity of UC. As artificial intelligence (AI) grows in popularity there is a growing interest in the use of these algorithms for diagnosing and classifying the IBDs and managing their progression. To tackle this, more annotated colonoscopy images and videos will be required for the training of new and more reliable AI algorithms. This article discusses the current challenges in the early detection of IBD, focusing on the available AI algorithms, and databases, and the challenges ahead to improve the detection rate.

Artificial intelligence based real time colorectal cancer screening study: Polyp segmentation and classification using multi-house database

PurposeThis work explores the potential of artificial intelligence (AI) for real-time colorectal cancer (CRC) screening. We utilized a multi-house dataset to achieve a three-step approach (i) automated polyp segmentation, (ii) analysis of machine learning (ML) for classification and (iii) to compare deep learning (DL) exploration for automated classification. MethodsTo achieve our objectives, we leveraged the CRPU-Net architecture for automated polyp segmentation within the multi-house colonoscopy images. Subsequently, a ML framework was employed for binary classification of the segmented regions. Feature extraction was performed using SIFT and ORB descriptors, followed by the feature fusion and dimensionality reduction through Principal Component Analysis (PCA) to create an optimal feature representation for classification. We further explored the potential of DL by investigating the use of SOTA and vision transformer (ViT) model. Additionally, we considered the development of a hybrid model (CRP-ViT) by potentially combining the ViT model with DL architectures. ResultsCRPU-Net achieved outstanding segmentation performance of 96.56% accuracy, 95.40% IoU, and 97.39% Dice coefficient. Additionally, the CRP-ViT model demonstrated promising results in binary classification of 96.59% accuracy, 96.38% sensitivity, 96.80% specificity, 96.82% precision, and 96.36% NPV. ConclusionThis study implemented CRPU-Net and CRP-ViT, AI systems designed for real-time polyp segmentation, and potential precancerous polyp classification. This comprehensive approach offers the potential to expedite colonoscopies, minimize unnecessary biopsies, improve patient care, and optimize healthcare resource allocation.

A complete benchmark for polyp detection, segmentation and classification in colonoscopy images.

Colorectal cancer (CRC) is one of the main causes of deaths worldwide. Early detection and diagnosis of its precursor lesion, the polyp, is key to reduce its mortality and to improve procedure efficiency. During the last two decades, several computational methods have been proposed to assist clinicians in detection, segmentation and classification tasks but the lack of a common public validation framework makes it difficult to determine which of them is ready to be deployed in the exploration room. This study presents a complete validation framework and we compare several methodologies for each of the polyp characterization tasks. Results show that the majority of the approaches are able to provide good performance for the detection and segmentation task, but that there is room for improvement regarding polyp classification. While studied show promising results in the assistance of polyp detection and segmentation tasks, further research should be done in classification task to obtain reliable results to assist the clinicians during the procedure. The presented framework provides a standarized method for evaluating and comparing different approaches, which could facilitate the identification of clinically prepared assisting methods.

How to "pick up" colorectal serrated lesions and polyps in daily histopathology practice: From terminologies to diagnostic pitfalls.

Over the last decade, our knowledge of colorectal serrated polyps and lesions has significantly improved due to numerous studies on this group of precursor lesions. Serrated lesions were misleading as benign before 2010, but they are currently reclassified as precancerous lesions that contribute to 30% of colorectal cancer through the serrated neoplasia pathway. The World Health Organization updated the classification for serrated lesions and polyps of the colon and rectum in 2019, which is more concise and applicable in daily practice. The responsible authors prescribe that "colorectal serrated lesions and polyps are characterized by a serrated (sawtooth or stellate) architecture of the epithelium." From a clinical standpoint, sessile serrated lesion (SSL) and SSL with dysplasia (SSLD) are the two most significant entities. Despite these advancements, the precise diagnosis of SSL and SSLD based mainly on histopathology remains challenging due to various difficulties. This review describes the nomenclature and the terminology of colorectal serrated polyps and lesions and highlights the diagnostic criteria and obstacles encountered in the histopathological diagnosis of SSL and SSLD.

Clinical characteristics of appendix orifice polyps and the effect endoscopic super minimally invasive treatment

To analyze the clinical characteristics of appendiceal orifice polyps and the effect of endoscopic super minimally invasive treatment. A retrospective analysis was conducted on the general situation (age and sex), the classification of appendiceal orifice polyps, the treatment method under endoscope, postoperative pathology and postoperative complications in patients who underwent resection of appendiceal orifice polyps at the Peking University First Hospital and the First Medical Center of the PLA General Hospital from January 1, 2022, to December 31, 2023. A total of 47 patients were included, consisting of 28 males and 19 females, with 35-86 (61±12) years. Appendiceal orifice polyps were classified into four types: type 0 (14 cases), type 1 (15 cases), type 2 (12 cases), and type 3 (6 cases). Among the endoscopic morphologies, 22 cases were granular laterally spreading tumors. Endoscopic mucosal dissection was performed in 37 cases. Postoperative appendiceal stent placement was performed in 1 case. The pathological types of polyps included adenoma in 15 cases, high-grade intraepithelial neoplasia in 10 cases, intramucosal carcinoma in 4 cases, submucosal carcinoma in 5 cases, inflammatory polyps in 1 cases, and sessile serrated lesion in 12 cases. Curative resection was performed in 44 cases. There were no postoperative complications such as bleeding, perforation, or acute appendicitis. The pathology of appendiceal polyps is mostly precancerous lesions, and the treatment scheme of endoscopic super minimally invasive resection is both safe and effective.

Novel classification of gastric polyps: The good, the bad and the ugly.

Gastric polyps (GPs) are increasingly common. On upper endoscopy, they should be examined with white light and occasionally chromoendoscopy, and their morphology classified according to the Paris classification. Most GPs have a typical endoscopic appearance and can be associated with diseases like Helicobacter pylori infection. Histological examination is necessary for an accurate diagnosis. While most polyps are non-neoplastic and do not require treatment, some carry a risk of malignancy or are already malignant. Therefore, understanding the diagnosis, classification, and management of GPs is crucial for patient prognostication. Our new classification categorizes GPs into "good", "bad", and "ugly" based on their likelihood of becoming malignant. We aim to provide descriptions of the endoscopic appearance, pathology, treatment, and follow-up for different GPs, as well as clinical management flowcharts.

A Boundary-Enhanced Decouple Fusion Segmentation Network for Diagnosis of Adenomatous Polyps.

Adenomatous polyps, a common premalignant lesion, are often classified into villous adenoma (VA) and tubular adenoma (TA). VA has a higher risk of malignancy, whereas TA typically grows slowly and has a lower likelihood of cancerous transformation. Accurate classification is essential for tailored treatment. In this study, we develop a deep learning-based approach for the localization and classification of adenomatous polyps using endoscopic images. Specifically, a pre-trained EGE-UNet is first adopted to extract regions of interest from original images. Multi-level feature maps are then extracted by the feature extraction pipeline (FEP). The deep-level features are fed into the Pyramid Pooling Module (PPM) to capture global contextual information, and the squeeze body edge (SBE) module is then used to decouple the body and edge parts of features, enabling separate analysis of their distinct characteristics. The Group Aggregation Bridge (GAB) and Boundary Enhancement Module (BEM) are then applied to enhance the body features and edge features, respectively, emphasizing their structural and morphological characteristics. By combining the features of the body and edge parts, the final output can be obtained. Experiments show the proposed method achieved promising results on two private datasets. For adenoma vs. non-adenoma classification, It achieved a mIoU of 91.41%, mPA of 96.33%, mHD of 11.63, and mASD of 2.33. For adenoma subclassification (non-adenomas vs. villous adenomas vs. tubular adenomas), it achieved a mIoU of 91.21%, mPA of 94.83%, mHD of 13.75, and mASD of 2.56. These results demonstrate the potential of our approach for precise adenomatous polyp classification.

A Novel Ensemble Deep Learning Based Polyp Detection Using Colonoscopy Dataset

This work addresses the critical task of polyp detection and classification using the SUN colonoscopy video database, which consists of still images annotated with bounding boxes. These images categorize frames into polyp and non-polyp and encompass six distinct classes of polyps: Hyperplastic polyp, Sessile serrated lesion, Low-grade adenoma, Traditional serrated adenoma, High-grade adenoma, and Invasive carcinoma. The approach involves a two-stage classification process. Initially, MobileNetV2 is employed to distinguish between polyp and non-polyp frames. Subsequently, ResNet50 and GoogLeNet are utilized to classify the identified polyps into the six predefined categories. Data augmentation techniques are implemented to address the inherent imbalance in class distribution within the dataset, enhancing model performance and generalizability. The results highlight the effectiveness of GoogLeNet, which achieved an impressive accuracy of 98%, significantly outperforming ResNet50's accuracy of 76.16%. This substantial improvement underscores the potential of GoogLeNet in enhancing the accuracy of polyp classification. The significance of this work lies in its contribution to advancing automated polyp detection and cancer stage classification, crucial for early diagnosis and treatment. These findings provide a foundation for further research and development in this domain, with the potential to improve clinical outcomes through more accurate and timely identification of colorectal polyps.

Standardized diagnosis and treatment of colorectal polyps

This article explores the standardized management of colorectal polyps, including classification, treatment, follow-up, and preventive control. Corresponding treatment strategies, including endoscopic resection and surgical intervention, are employed for different types of polyps. Currently, there is debate over whether to choose endoscopic resection or surgical intervention for malignant polyps at pT1 stage. Drawing on the latest literature and guidelines, the article elaborates on polyp classification, treatment modalities, follow-up, and preventive measures. Standardized management of colorectal polyps is important for reducing the incidence of colorectal cancer and improving the cure rate of early-stage colorectal cancer.

MEGANET: Improved framework with nature inspired approach for colorectal cancer polyp classification

BACKGROUND: Polyps are tumorous growths in the colon or rectal area which can turn into cancer at later stages, thus detection of polyps is very important for timely prevention of colorectal cancer. The aim of the study is to develop a framework to accurately detect polyp images in colonoscopy images. OBJECTIVE: Development of an intelligent framework for classification of colorectal cancer from colon and rectal images. The standard machine learning, convolutional neural networks and ensemble models with nature inspired approach were implemented for this study. Model optimization was performed by varying hyper parameters. The main objective was to find an optimal model with high accuracy, optimized weights and less parameters. METHODS: The deep learning Convolutional Neural Network (CNN) models such as VGG19, ResNet50, EfficientNet, Ensemble Model (EM), and Modified Ensemble CNN with Genetic Algorithm (MEGANET) were implemented for the classification of colon images. RESULTS: Ensemble model was also created with two best performing deep learning models to further achieve higher accuracy of 96%. The ensemble model outperformed the other models in terms of accuracy, precision, recall, and F1 score. But this model has more complexity. The MEGANET, nature inspired evolutionary ensemble CNN model was implemented with transfer learning and genetic algorithms for weights optimization and parameter reduction. It achieved accuracy of 95%, on training data. CONCLUSION: The MEGANET performed similar to EM with less number of parameters on training, validation and test dataset. In future different methods will be implemented to further reduce the parameters and attain reasonable accuracy using MEGANET.

Comparative analysis of machine learning frameworks for automatic polyp characterization

Early detection and characterization of adenomatous polyps, the precursors to colorectal cancer is crucial for effective diagnosis and treatment of colorectal cancer. A suitably trained machine learning algorithm can perform automatic real-time optical characterization of polyps during colonoscopy procedures with virtual chromoendoscopy systems, such as Narrow Band Imaging (NBI). This study proposes to perform a detailed comparative analysis between the performances of two popular machine learning frameworks, namely training from scratch and transfer learning, using three state-of-the-art architectures ResNet50, EfficientNetB2, and ViTb_32 with one indigenous dataset and one publicly available dataset. The indigenous dataset was created using 1739 NBI frames extracted from colonoscopy videos of 30 polyp masses from 24 patients [870 hyperplastic polyp frames and 869 adenomatous polyp frames]. The publicly available dataset also consisted of NBI frames. Blurry frames or frames with specular reflections were not excluded to closely resemble real-life polyp characterization in a clinical setting. Among the three models used in two different frameworks, EfficientNetB2 in the training from scratch framework yielded the best accuracy, AUC and NPV of 91.91%, 0.99 and 89.06%. The results show that the training from scratch framework outperformed the transfer learning framework in terms of the evaluation metrics. To the best of our knowledge, this is the first indigenous study to focus on polyp classification using solely Narrow Band Imaging (NBI) polyp images. Our results using only NBI polyp frames outperformed those obtained with a combination of white light and NBI frames, as observed in similar existing studies.

Establishment and validation of an artificial intelligence-based model for real-time detection and classification of colorectal adenoma

Colorectal cancer (CRC) prevention requires early detection and removal of adenomas. We aimed to develop a computational model for real-time detection and classification of colorectal adenoma. Computationally constrained background based on real-time detection, we propose an improved adaptive lightweight ensemble model for real-time detection and classification of adenomas and other polyps. Firstly, we devised an adaptive lightweight network modification and effective training strategy to diminish the computational requirements for real-time detection. Secondly, by integrating the adaptive lightweight YOLOv4 with the single shot multibox detector network, we established the adaptive small object detection ensemble (ASODE) model, which enhances the precision of detecting target polyps without significantly increasing the model's memory footprint. We conducted simulated training using clinical colonoscopy images and videos to validate the method's performance, extracting features from 1148 polyps and employing a confidence threshold of 0.5 to filter out low-confidence sample predictions. Finally, compared to state-of-the-art models, our ASODE model demonstrated superior performance. In the test set, the sensitivity of images and videos reached 87.96% and 92.31%, respectively. Additionally, the ASODE model achieved an accuracy of 92.70% for adenoma detection with a false positive rate of 8.18%. Training results indicate the effectiveness of our method in classifying small polyps. Our model exhibits remarkable performance in real-time detection of colorectal adenomas, serving as a reliable tool for assisting endoscopists.

Typicality- and instance-dependent label noise-combating: a novel framework for simulating and combating real-world noisy labels for endoscopic polyp classification

Learning with noisy labels aims to train neural networks with noisy labels. Current models handle instance-independent label noise (IIN) well; however, they fall short with real-world noise. In medical image classification, atypical samples frequently receive incorrect labels, rendering instance-dependent label noise (IDN) an accurate representation of real-world scenarios. However, the current IDN approaches fail to consider the typicality of samples, which hampers their ability to address real-world label noise effectively. To alleviate the issues, we introduce typicality- and instance-dependent label noise (TIDN) to simulate real-world noise and establish a TIDN-combating framework to combat label noise. Specifically, we use the sample’s distance to decision boundaries in the feature space to represent typicality. The TIDN is then generated according to typicality. We establish a TIDN-attention module to combat label noise and learn the transition matrix from latent ground truth to the observed noisy labels. A recursive algorithm that enables the network to make correct predictions with corrections from the learned transition matrix is proposed. Our experiments demonstrate that the TIDN simulates real-world noise more closely than the existing IIN and IDN. Furthermore, the TIDN-combating framework demonstrates superior classification performance when training with simulated TIDN and actual real-world noise.

Paris classification of colonic polyps using CT colonography: prospective cohort study of interobserver variation.

The Paris classification categorises colorectal polyp morphology. Interobserver agreement for Paris classification has been assessed at optical colonoscopy (OC) but not CT colonography (CTC). We aimed to determine the following: (1) interobserver agreement for the Paris classification using CTC between radiologists; (2) if radiologist experience influenced classification, gross polyp morphology, or polyp size; and (3) the extent to which radiologist classifications agreed with (a) colonoscopy and (b) a combined reference standard. Following ethical approval for this non-randomised prospective cohort study, seven radiologists from three hospitals classified 52 colonic polyps using the Paris system. We calculated interobserver agreement using Fleiss kappa and mean pairwise agreement (MPA). Absolute agreement was calculated between radiologists; between CTC and OC; and between CTC and a combined reference standard using all available imaging, colonoscopic, and histopathological data. Overall interobserver agreement between the seven readers was fair (Fleiss kappa 0.33; 95% CI 0.30-0.37; MPA 49.7%). Readers with < 1500 CTC experience had higher interobserver agreement (0.42 (95% CI 0.35-0.48) vs. 0.33 (95% CI 0.25-0.42)) and MPA (69.2% vs 50.6%) than readers with ≥ 1500 experience. There was substantial overall agreement for flat vs protuberant polyps (0.62 (95% CI 0.56-0.68)) with a MPA of 87.9%. Agreement between CTC and OC classifications was only 44%, and CTC agreement with the combined reference standard was 56%. Radiologist agreement when using the Paris classification at CT colonography is low, and radiologist classification agrees poorly with colonoscopy. Using the full Paris classification in routine CTC reporting is of questionable value. Interobserver agreement for radiologists using the Paris classification to categorise colorectal polyp morphology is only fair; routine use of the full Paris classification at CT colonography is questionable. • Overall interobserver agreement for the Paris classification at CT colonography (CTC) was only fair, and lower than for colonoscopy. • Agreement was higher for radiologists with < 1500 CTC experience and for larger polyps. There was substantial agreement when classifying polyps as protuberant vs flat. • Agreement between CTC and colonoscopic polyp classification was low (44%).

An effective colorectal polyp classification for histopathological images based on supervised contrastive learning

Early detection of colon adenomatous polyps is pivotal in reducing colon cancer risk. In this context, accurately distinguishing between adenomatous polyp subtypes, especially tubular and tubulovillous, from hyperplastic variants is crucial. This study introduces a cutting-edge computer-aided diagnosis system optimized for this task. Our system employs advanced Supervised Contrastive learning to ensure precise classification of colon histopathology images. Significantly, we have integrated the Big Transfer model, which has gained prominence for its exemplary adaptability to visual tasks in medical imaging. Our novel approach discerns between in-class and out-of-class images, thereby elevating its discriminatory power for polyp subtypes. We validated our system using two datasets: a specially curated one and the publicly accessible UniToPatho dataset. The results reveal that our model markedly surpasses traditional deep convolutional neural networks, registering classification accuracies of 87.1% and 70.3% for the custom and UniToPatho datasets, respectively. Such results emphasize the transformative potential of our model in polyp classification endeavors.

Recognition of Colon Polyps (Tubular Adenoma, Villous Adenoma) and Normal Colon Epithelium Histomorphology with Transfer Learning.

The use of artificial intelligence technology in medicine, which is remarkable with its increasing use in many areas recently, has allowed for rapid developments. This technology quantitatively solves many problems in medicine, such as increased workload, delayed diagnosis, and treatment processes. It has been seen in the literature that this technology, which has a wide range of applications in medicine, also has a place in medical pathology. The main purpose of this study is to provide a histomorphological classification of colon polyps in the medical pathology department with high accuracy in a short time. Besides accelerating the diagnosis and treatment process, it is desired to facilitate the workload of the pathology department. This study is based on the recognition of colon preparation images that come to the pathology department by using the image recognition techniques of artificial intelligence. VGG19, DenseNet201, and EfcientNetB7 models, which are convolutional neural networks (CNN) models, were used. A model based on a concatenation ensemble of 3 CNN architectures was also used in this study. Within the scope of the study, a total of 515 preparation images, including normal epithelium, tubular adenoma and villous adenomas of the colon, were procured as data and introduced to artificial intelligence, and the diagnoses of these preparations were estimated histomorphologically by artificial intelligence. As a result of the study, an accuracy rate of 94.17% was achieved.

A colonial serrated polyp classification model using white-light ordinary endoscopy images with an artificial intelligence model and TensorFlow chart

In this study, we implemented a combination of data augmentation and artificial intelligence (AI) model—Convolutional Neural Network (CNN)—to help physicians classify colonic polyps into traditional adenoma (TA), sessile serrated adenoma (SSA), and hyperplastic polyp (HP). We collected ordinary endoscopy images under both white and NBI lights. Under white light, we collected 257 images of HP, 423 images of SSA, and 60 images of TA. Under NBI light, were collected 238 images of HP, 284 images of SSA, and 71 images of TA. We implemented the CNN-based artificial intelligence model, Inception V4, to build a classification model for the types of colon polyps. Our final AI classification model with data augmentation process is constructed only with white light images. Our classification prediction accuracy of colon polyp type is 94%, and the discriminability of the model (area under the curve) was 98%. Thus, we can conclude that our model can help physicians distinguish between TA, SSA, and HPs and correctly identify precancerous lesions such as TA and SSA.

An Efficient Multi-Task Synergetic Network for Polyp Segmentation and Classification.

Colonoscopy is considered the best diagnostic tool for early detection and resection of polyps, which can effectively prevent consequential colorectal cancer. In clinical practice, segmenting and classifying polyps from colonoscopic images have a great significance since they provide precious information for diagnosis and treatment. In this study, we propose an efficient multi-task synergetic network (EMTS-Net) for concurrent polyp segmentation and classification, and we introduce a polyp classification benchmark for exploring the potential correlations of the above-mentioned two tasks. This framework is composed of an enhanced multi-scale network (EMS-Net) for coarse-grained polyp segmentation, an EMTS-Net (Class) for accurate polyp classification, and an EMTS-Net (Seg) for fine-grained polyp segmentation. Specifically, we first obtain coarse segmentation masks by using EMS-Net. Then, we concatenate these rough masks with colonoscopic images to assist EMTS-Net (Class) in locating and classifying polyps precisely. To further enhance the segmentation performance of polyps, we propose a random multi-scale (RMS) training strategy to eliminate the interference caused by redundant information. In addition, we design an offline dynamic class activation mapping (OFLD CAM) generated by the combined effect of EMTS-Net (Class) and RMS strategy, which optimizes bottlenecks between multi-task networks efficiently and elegantly and helps EMTS-Net (Seg) to perform more accurate polyp segmentation. We evaluate the proposed EMTS-Net on the polyp segmentation and classification benchmarks, and it achieves an average mDice of 0.864 in polyp segmentation and an average AUC of 0.913 with an average accuracy of 0.924 in polyp classification. Quantitative and qualitative evaluations on the polyp segmentation and classification benchmarks demonstrate that our EMTS-Net achieves the best performance and outperforms previous state-of-the-art methods in terms of both efficiency and generalization.

Endoluminal and next generation robotics in colorectal surgery

Innovative approaches leverage cutting-edge technologies to advance the field of minimally invasive colorectal surgery. The implementation of complex endoluminal and transluminal procedures is challenging and requires extensive training. Recent technological advances not only enable improved detection of colorectal neoplasms with the support of computer science, but also enhanced surgical treatment with robotic instrumentation. Early diagnosis will revolutionize colorectal cancer treatment by avoiding surgery, as has been demonstrated in the treatment of early-stage gastric cancer using third-space endoscopy. Automated colorectal cancer screening with polyp detection and classification can change the therapeutic strategy and orient towards resection during endoscopy, including full-thickness resection, with endoscopic bimanual suturing to close the defect. Several therapeutic flexible endoscopic robotic systems are expected to provide surgery-like dexterity for endo- and transluminal approaches by enabling tissue triangulation while lowering the technical hurdles for endoscopic submucosal dissection and other highly sophisticated procedures such as endoscopic creation or revision of anastomoses. In the curvilinear anatomy of the colon and rectum, navigation assistance for improved spatio-temporal orientation will aid in the detection and documentation of lesions and facilitate their localization for excision and follow-up. Flexible robotic systems encourage surgeons as well as gastroenterologists to perform advanced intra- and transluminal resections.

Accuracy of GPT-4 in histopathological image detection and classification of colorectal adenomas

AimsTo evaluate the accuracy of Chat Generative Pre-trained Transformer (ChatGPT) powered by GPT-4 in histopathological image detection and classification of colorectal adenomas using the diagnostic consensus provided by pathologists as...