Colonoscopy polyp detection and classification: Dataset creation and comparative evaluations

Li, Kaidong; Fathan, Mohammad Isyroqi; Patel, Krushi; Zhang, Tianxiao; Zhong, Cuncong; Bansal, Ajay; Rastogi, Amit; Wang, Jean S.; Wang, Guanghui

doi:10.1371/journal.pone.0255809

Cited by 65 publications

(24 citation statements)

References 75 publications

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“…In this study, the diagnoses of FOBT-positive subjects were polyps in 52% of subjects. In the literature, 95% of CRC cases are associated with adenomatous polyps [59][60][61][62], and the process of malignant transformation of the polyps may take up to 10 years [63]. Therefore, promoting FOBT implementation, understanding the characteristics of FOBT-positive people, and enhancing active follow-up diagnosis are important health policies for CRC prevention.…”

Section: Discussionmentioning

confidence: 99%

Examining the Factors That Affect the Diagnosis of Patients with Positive Fecal Occult Blood Test Results

Cheng

2022

IJERPH

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Due to the threat of colorectal cancer (CRC) to health, Taiwan included the fecal occult blood test (FOBT) under preventive health services in 2010. We examined the factors that affect the diagnosis of people with positive FOBT results. Data were retrospectively collected from the CRC screening database. In the model predicting factors that affect the diagnosis of 89,046 people with positive FOBT results, the risks of disease in the CRC group were lower in medical institutions that conducted follow-up examinations in regions such as Northern Taiwan compared to that in Eastern Taiwan (p = 0.013); they were lower in the age group of 50 to 65 years than those in the age group of 71 to 75 years (p < 0.001, p = 0.016), and lower in the outpatient medical units that conducted follow-up examinations than those in the inpatient medical units by 0.565 times (p < 0.001, 95% CI: 0.493–0.647). Factors affecting the diagnosis of patients with positive FOBT results were gender, the region of the medical institution, medical unit for follow-up examinations, age, screening site, family history, type of follow-up examinations, and follow-up time. Therefore, the identification of characteristics of patients with positive FOBT results and the promotion of follow-up examination are important prevention strategies for CRC.

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Section: Discussionmentioning

confidence: 99%

Examining the Factors That Affect the Diagnosis of Patients with Positive Fecal Occult Blood Test Results

Cheng

2022

IJERPH

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“…Liu et al [24] applied different CNN backbones including InceptionV3 [37], ResNet50 [11] and VGG16 [36] to the SSD framework, whose accuracy was much higher than other one-stage object detectors and comparable to the Faster-RCNN two-stage one. In [23], Li et al compared the performance of eight state-of-the-art deep learning object detectors and demonstrated promising results in colonoscopy.…”

Section: Related Workmentioning

confidence: 99%

BlazeNeo: Blazing fast polyp segmentation and neoplasm detection

An¹,

Lan²,

Hang³

et al. 2022

Preprint

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In recent years, computer-aided automatic polyp segmentation and neoplasm detection have been an emerging topic in medical image analysis, providing valuable support to colonoscopy procedures. Attentions have been paid to improving the accuracy of polyp detection and segmentation. However, not much focus has been given to latency and throughput for performing these tasks on dedicated devices, which can be crucial for practical applications. This paper introduces a novel deep neural network architecture called BlazeNeo, for the task of polyp segmentation and neoplasm detection with an emphasis on compactness and speed while maintaining high accuracy. The model leverages the highly efficient HarDNet backbone alongside lightweight Receptive Field Blocks for computational efficiency, and an auxiliary training mechanism to take full advantage of the training data for the segmentation quality. Our experiments on a challenging dataset show that BlazeNeo achieves improvements in latency and model size while maintaining comparable accuracy against state-of-the-art methods. When deploying on the Jetson AGX Xavier edge device in INT8 precision, our BlazeNeo achieves over 155 fps while yielding the best accuracy among all compared methods.

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“…Another study in [78] created an endoscopic dataset from different sources and annotated the ground truths by collaborating with experienced gastroenterologists. Due to the severe differences in the existing datasets in terms of image resolution and color temperature (possibly due to different imaging equipment setups), the authors built a new dataset to serve as a benchmark to train and evaluate the DL models for polyp detection and classification.…”

Section: Polyp Detectionmentioning

confidence: 99%

Deep Learning Approaches to Colorectal Cancer Diagnosis: A Review

Tamang

Kim

2021

Applied Sciences

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Unprecedented breakthroughs in the development of graphical processing systems have led to great potential for deep learning (DL) algorithms in analyzing visual anatomy from high-resolution medical images. Recently, in digital pathology, the use of DL technologies has drawn a substantial amount of attention for use in the effective diagnosis of various cancer types, especially colorectal cancer (CRC), which is regarded as one of the dominant causes of cancer-related deaths worldwide. This review provides an in-depth perspective on recently published research articles on DL-based CRC diagnosis and prognosis. Overall, we provide a retrospective synopsis of simple image-processing-based and machine learning (ML)-based computer-aided diagnosis (CAD) systems, followed by a comprehensive appraisal of use cases with different types of state-of-the-art DL algorithms for detecting malignancies. We first list multiple standardized and publicly available CRC datasets from two imaging types: colonoscopy and histopathology. Secondly, we categorize the studies based on the different types of CRC detected (tumor tissue, microsatellite instability, and polyps), and we assess the data preprocessing steps and the adopted DL architectures before presenting the optimum diagnostic results. CRC diagnosis with DL algorithms is still in the preclinical phase, and therefore, we point out some open issues and provide some insights into the practicability and development of robust diagnostic systems in future health care and oncology.

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Colonoscopy polyp detection and classification: Dataset creation and comparative evaluations

Cited by 65 publications

References 75 publications

Examining the Factors That Affect the Diagnosis of Patients with Positive Fecal Occult Blood Test Results

Examining the Factors That Affect the Diagnosis of Patients with Positive Fecal Occult Blood Test Results

BlazeNeo: Blazing fast polyp segmentation and neoplasm detection

Deep Learning Approaches to Colorectal Cancer Diagnosis: A Review

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