Deep learning to find colorectal polyps in colonoscopy: A systematic literature review

Colorectal cancer has a great incidence rate worldwide, but its early detection significantly increases the survival rate. Colonoscopy is the gold standard procedure for diagnosis and removal of colorectal lesions with potential to evolve into cancer and computer-aided detection systems can help gastroenterologists to increase the adenoma detection rate, one of the main indicators for colonoscopy quality and predictor for colorectal cancer prevention. The recent success of deep learning approaches in computer vision has also reached this field and has boosted the number of proposed methods for polyp detection, localization and segmentation. Through a systematic search, 35 works have been retrieved. The current systematic review provides an analysis of these methods, stating advantages and disadvantages for the different categories used; comments seven publicly available datasets of colonoscopy images; analyses the metrics used for reporting and identifies future challenges and recommendations. Convolutional neural networks are the most used architecture together with an important presence of data augmentation strategies, mainly based on image transformations and the use of patches. End-to-end methods are preferred over hybrid methods, with a rising tendency. As for detection and localization tasks, the most used metric for reporting is the recall, while Intersection over Union is highly used in segmentation. One of the major concerns is the difficulty for a fair comparison and reproducibility of methods. Even despite the organization of challenges, there is still a need for a common validation framework based on a large, annotated and publicly available database, which also includes the most convenient metrics to report results. Finally, it is also important to highlight that efforts should be focused in the future on proving the clinical value of the deep learning based methods, by increasing the adenoma detection rate.This work was partially supported by PICCOLO project. This project has received funding from the European Union's Horizon2020 Research and Innovation Programme under grant agreement No. 732111. The sole responsibility of this publication lies with the author. The European Union is not responsible for any use that may be made of the information contained therein. The authors would also like to thank Dr. Federico Soria for his support on this manuscript and Dr. José Carlos Marín, from Hospital 12 de Octubre, and Dr. Ángel Calderón and Dr. Francisco Polo, from Hospital de Basurto, for the images in Fig. 4

Classification of Colorectal Cancer Polyps via Transfer Learning and Vision-Based Tactile Sensing

In this study, to address the current high earlydetection miss rate of colorectal cancer (CRC) polyps, we explore the potentials of utilizing transfer learning and machine learning (ML) classifiers to precisely and sensitively classify the type of CRC polyps. Instead of using the common colonoscopic images, we applied three different ML algorithms on the 3D textural image outputs of a unique vision-based surface tactile sensor (VS-TS). To collect realistic textural images of CRC polyps for training the utilized ML classifiers and evaluating their performance, we first designed and additively manufactured 48 types of realistic polyp phantoms with different hardness, type, and textures. Next, the performance of the used three ML algorithms in classifying the type of fabricated polyps was quantitatively evaluated using various statistical metrics.Comment: Accepted to IEEE Sensors 2022 Conferenc

Automatic detection of colorectal polyps using artificial intelligence techniques

Colorectal cancer (CRC) is one of the most prevalent malignant tumors in Colombia and the world. These neoplasms originate in adenomatous lesions or polyps that must be resected to prevent the disease, which can be done with a colonoscopy. It has been reported that during colonoscopy polyps are detected in 40% of men and 30% of women (hyperplastic, adenomatous, serrated, among others), and, on average, 25% of adenomatous polyps (main quality indicator in colonoscopy). However, these lesions are not easy to observe due to the multiplicity of blind spots in the colon and the human error associated with the examination. Objective: to create a computational method for the automatic detection of colorectal polyps using artificial intelligence in recorded videos of real colonoscopy procedures. Methodology: public databases with colorectal polyps and a data collection built in a University Hospital were used. Initially, all the frames of the videos were normalized to reduce the high variability between databases. Subsequently, the polyp detection task is done with a deep learning method using a convolutional neural network. This network is initialized with weights learned on millions of national images from the ImageNet database. The weights of the network are updated using colonoscopy images, following the tuning technique.

Towards Reliable Colorectal Cancer Polyps Classification via Vision Based Tactile Sensing and Confidence-Calibrated Neural Networks

In this study, toward addressing the over-confident outputs of existing artificial intelligence-based colorectal cancer (CRC) polyp classification techniques, we propose a confidence-calibrated residual neural network. Utilizing a novel vision-based tactile sensing (VS-TS) system and unique CRC polyp phantoms, we demonstrate that traditional metrics such as accuracy and precision are not sufficient to encapsulate model performance for handling a sensitive CRC polyp diagnosis. To this end, we develop a residual neural network classifier and address its over-confident outputs for CRC polyps classification via the post-processing method of temperature scaling. To evaluate the proposed method, we introduce noise and blur to the obtained textural images of the VS-TS and test the model's reliability for non-ideal inputs through reliability diagrams and other statistical metrics

Enhancing endoscopic navigation and polyp detection using artificial intelligence

Colorectal cancer (CRC) is one most common and deadly forms of cancer. It has a very high mortality rate if the disease advances to late stages however early diagnosis and treatment can be curative is hence essential to enhancing disease management. Colonoscopy is considered the gold standard for CRC screening and early therapeutic treatment. The effectiveness of colonoscopy is highly dependent on the operator’s skill, as a high level of hand-eye coordination is required to control the endoscope and fully examine the colon wall. Because of this, detection rates can vary between different gastroenterologists and technology have been proposed as solutions to assist disease detection and standardise detection rates. This thesis focuses on developing artificial intelligence algorithms to assist gastroenterologists during colonoscopy with the potential to ensure a baseline standard of quality in CRC screening. To achieve such assistance, the technical contributions develop deep learning methods and architectures for automated endoscopic image analysis to address both the detection of lesions in the endoscopic image and the 3D mapping of the endoluminal environment. The proposed detection models can run in real-time and assist visualization of different polyp types. Meanwhile the 3D reconstruction and mapping models developed are the basis for ensuring that the entire colon has been examined appropriately and to support quantitative measurement of polyp sizes using the image during a procedure. Results and validation studies presented within the thesis demonstrate how the developed algorithms perform on both general scenes and on clinical data. The feasibility of clinical translation is demonstrated for all of the models on endoscopic data from human participants during CRC screening examinations

Mapas de atención para destacar pólipos potenciales durante la colonoscopia

Context: Polyps are protruding masses that grow along the intestinal tract and are considered to be the main precursors of colorectal cancer. In early stages, polyp detection represents a survival probability of up to 93%, whereas, for other stages, this probability can decrease to 8%. Despite the fact that colonoscopy is the most effective method to detect polyps, several studies have shown a loss rate of up to 26% in detecting polyps. Computer tools have emerged as an alternative to support polyp detection and localization, but various problems remain open due to their high variability. Method: This work introduces a computational strategy that produces visual attention maps with the most probable location of polyps to generate alarms and support detection procedures. Each colonoscopy frame is decomposed into a set of deep features extracted from pre-trained architectures. Such features are encoded into a dense Hough representation in order to obtain a polyp template, which is then propagated in each frame to obtain a visual attention map. The maximum regions are back-projected to the colonoscopy in order to draw suspicious polyp regions. Results: The proposed strategy was evaluated in the ASU-Mayo Clinic and CVC-Video Clinic datasets, reporting a detection accuracy of 70% among the four most probable regions, while ten regions yielded 80%. Conclusions: The obtained attention maps highlight the most probable regions with suspicious polyps. The proposed approach may be useful to support colonoscopy analysis.Contexto: Los pólipos son masas protuberantes que crecen a lo largo del tracto intestinal y se consideran los principales precursores del cáncer de colon. En las etapas tempranas, la detección de pólipos representa una probabilidad de supervivencia de hasta el 93%, mientras que, en otras etapas, esta probabilidad disminuye hasta el 8%. A pesar de que la colonoscopia es el método más efectivo para detectar pólipos, varios estudios han demostrado una tasa de pérdida de hasta el 26% en la detección pólipos. Las herramientas computacionales han surgido como una alternativa para soportar la detección y localización de pólipos, pero varios problemas siguen abiertos debido a la alta variabilidad de los mismos. Método: Este trabajo introduce una estrategia computacional que produce mapas de atención visual con la localización más probable de los pólipos para generar alarmas y apoyar la tarea de detección. Cada fotograma de colonoscopia se descompone en un conjunto de características profundas extraídas de arquitecturas preentrenadas. Dichas características se codifican en una representación densa de Hough para obtener una plantilla del pólipo, que posteriormente se propaga en cada fotograma para obtener los mapas de atención visual. Las regiones máximas son proyectadas a la colonoscopia para dibujar las regiones sospechosas de pólipo. Resultados:  La estrategia propuesta fue evaluada en los conjuntos de datos ASU-Mayo Clinic y CVC-Video Clinic, reportando una exactitud de 70% de detección entre las cuatro regiones más probables, mientras que con diez regiones se tiene un 80%. Conclusiones:  Los mapas de atención obtenidos destacan las regiones más probables con pólipos. El enfoque propuesto puede ser útil para apoyar el análisis de la colonoscopia

A deep learning framework to classify breast density with noisy labels regularization

Background and objective: Breast density assessed from digital mammograms is a biomarker for higher risk of developing breast cancer. Experienced radiologists assess breast density using the Breast Image and Data System (BI-RADS) categories. Supervised learning algorithms have been developed with this objective in mind, however, the performance of these algorithms depends on the quality of the ground-truth information which is usually labeled by expert readers. These labels are noisy approximations of the ground truth, as there is often intra- and inter-reader variability among labels. Thus, it is crucial to provide a reliable method to obtain digital mammograms matching BI-RADS categories. This paper presents RegL (Labels Regularizer), a methodology that includes different image pre-processes to allow both a correct breast segmentation and the enhancement of image quality through an intensity adjustment, thus allowing the use of deep learning to classify the mammograms into BI-RADS categories. The Confusion Matrix (CM) - CNN network used implements an architecture that models each radiologist's noisy label. The final methodology pipeline was determined after comparing the performance of image pre-processes combined with different DL architectures. Methods: A multi-center study composed of 1395 women whose mammograms were classified into the four BI-RADS categories by three experienced radiologists is presented. A total of 892 mammograms were used as the training corpus, 224 formed the validation corpus, and 279 the test corpus. Results: The combination of five networks implementing the RegL methodology achieved the best results among all the models in the test set. The ensemble model obtained an accuracy of (0.85) and a kappa index of 0.71. Conclusions: The proposed methodology has a similar performance to the experienced radiologists in the classification of digital mammograms into BI-RADS categories. This suggests that the pre-processing steps and modelling of each radiologist's label allows for a better estimation of the unknown ground truth labels.This work was partially funded by Generalitat Valenciana through IVACE (Valencian Institute of Business Competitiveness) distributed nominatively to Valencian technological innovation centres under project expedient IMAMCN/2021/1.S

Classifying Garments from Fashion-MNIST Dataset Through CNNs

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