Multi-task network for automated analysis of high-resolution endomicroscopy images to detect cervical precancer and cancer

Brenes, David; Barberan, CJ; Hunt, Brady; Parra, Sonia; Salcedo, Mila Pontremoli; Possati-Resende, Júlio César; Cremer, Miriam; Castle, Philip E.; Fregnani, José Humberto Tavares Guerreiro; Maza, Mauricio; Schmeler, Kathleen M.; Baraniuk, Richard G.; Richards‐Kortum, Rebecca

doi:10.1016/j.compmedimag.2022.102052

Cited by 18 publications

(9 citation statements)

References 43 publications

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“…This study does not assess the tradeoff of training and validating in our model using cervical instead of anal HRME images. Based on our previous assessment of the number of samples required to train the MTN for cervical precancer detection, we can infer that we currently do not have sufficient samples to train and validate the MTN exclusively on anal HRME data ( 13 ). However, future work could explore using anal HRME data to refine the MTN and improve its diagnostic performance once more data become available.…”

Section: Discussionmentioning

confidence: 99%

“…In this study, we applied the MTN optimized for detection of cervical precancer without modification to analyze HRME images of anal tissue. The MTN was trained and validated for detection of cervical precancer using histologically correlated cervical HRME images from over 1,600 screen-positive women ( 13 ). External validation in an additional set of 508 cervical patients confirmed that the model's performance was on par with expert coloscopic impression ( 13 ).…”

Section: Methodsmentioning

confidence: 99%

“…The MTN was used to analyze HRME images passing quality control ( 13 ). In brief, the MTN consists of a nuclear segmentation component and a diagnostic branch.…”

Section: Methodsmentioning

confidence: 99%

“…In this work, we describe the use of a novel automated machine learning method to analyze HRME images of anal epithelium ( 13 ). The novel deep learning algorithm was initially developed to detect cervical precancer in HRME images and achieved a performance comparable to expert colposcopy with a sensitivity of 0.94 ( P = 0.3) and specificity of 0.58 ( P = 1.0) ( 13 ). In this work, we collected HRME images from 77 patients living with HIV and analyzed them with the proposed algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Automated In Vivo High-Resolution Imaging to Detect Human Papillomavirus–Associated Anal Precancer in Persons Living With HIV

Brenes

Kortum

Carns

et al. 2022

Clin Transl Gastroenterol

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INTRODUCTION: In the United States, the effectiveness of anal cancer screening programs has been limited by a lack of trained professionals proficient in high-resolution anoscopy (HRA) and a high patient lost-to-follow-up rate between diagnosis and treatment. Simplifying anal intraepithelial neoplasia grade 2 or more severe (AIN 2+) detection could radically improve the access and efficiency of anal cancer prevention. Novel optical imaging providing point-of-care diagnoses could substantially improve existing HRA and histology-based diagnosis. This work aims to demonstrate the potential of high-resolution microendoscopy (HRME) coupled with a novel machine learning algorithm for the automated, in vivo diagnosis of anal precancer. METHODS: The HRME, a fiber-optic fluorescence microscope, was used to capture real-time images of anal squamous epithelial nuclei. Nuclear staining is achieved using 0.01% wt/vol proflavine, a topical contrast agent. HRME images were analyzed by a multitask deep learning network (MTN) that computed the probability of AIN 2+ for each HRME image. RESULTS: The study accrued data from 77 people living with HIV. The MTN achieved an area under the receiver operating curve of 0.84 for detection of AIN 2+. At the AIN 2+ probability cutoff of 0.212, the MTN achieved comparable performance to expert HRA impression with a sensitivity of 0.92 ( P = 0.68) and specificity of 0.60 ( P = 0.48) when using histopathology as the gold standard. DISCUSSION: When used in combination with HRA, this system could facilitate more selective biopsies and promote same-day AIN2+ treatment options by enabling real-time diagnosis.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…The MTN was used to analyze HRME images passing quality control ( 13 ). In brief, the MTN consists of a nuclear segmentation component and a diagnostic branch.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automated In Vivo High-Resolution Imaging to Detect Human Papillomavirus–Associated Anal Precancer in Persons Living With HIV

Brenes

Kortum

Carns

et al. 2022

Clin Transl Gastroenterol

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“…After each down sampling, up sampling, ESP, and PSP module, batch normalization and ReLU activation are implemented. The modules that make up the MTN and the learning via proxy labels are described in the following sections [38].…”

Section: Detection Methods Based On Cells/pap Smear Imagesmentioning

confidence: 99%

Improvement method for cervical cancer detection: A comparative analysis

Alias¹,

Mustafa²,

Jamlos³

et al. 2021

Oncology Research

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Cervical cancer is a prevalent and deadly cancer that affects women all over the world. It affects about 0.5 million women anually and results in over 0.3 million fatalities. Diagnosis of this cancer was previously done manually, which could result in false positives or negatives. The researchers are still contemplating how to detect cervical cancer automatically and how to evaluate Pap smear images. Hence, this paper has reviewed several detection methods from the previous researches that has been done before. This paper reviews pre-processing, detection method framework for nucleus detection, and analysis performance of the method selected. There are four methods based on a reviewed technique from previous studies that have been running through the experimental procedure using Matlab, and the dataset used is established Herlev Dataset. The results show that the highest performance assessment metric values obtain from Method 1: Thresholding and Trace region boundaries in a binary image with the values of precision 1.0, sensitivity 98.77%, specificity 98.76%, accuracy 98.77% and PSNR 25.74% for a single type of cell. Meanwhile, the average values of precision were 0.99, sensitivity 90.71%, specificity 96.55%, accuracy 92.91% and PSNR 16.22%. The experimental results are then compared to the existing methods from previous studies. They show that the improvement method is able to detect the nucleus of the cell with higher performance assessment values. On the other hand, the majority of current approaches can be used with either a single or a large number of cervical cancer smear images. This study might persuade other researchers to recognize the value of some of the existing detection techniques and offer a strong approach for developing and implementing new solutions.

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Data Augmentation Techniques to Detect Cervical Cancer Using Deep Learning: A Systematic Review

Wubineh,

Rusiecki,

Halawa

2024

Lecture Notes in Networks and Systems

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Multi-task network for automated analysis of high-resolution endomicroscopy images to detect cervical precancer and cancer

Cited by 18 publications

References 43 publications

Automated In Vivo High-Resolution Imaging to Detect Human Papillomavirus–Associated Anal Precancer in Persons Living With HIV

Automated In Vivo High-Resolution Imaging to Detect Human Papillomavirus–Associated Anal Precancer in Persons Living With HIV

Improvement method for cervical cancer detection: A comparative analysis

Data Augmentation Techniques to Detect Cervical Cancer Using Deep Learning: A Systematic Review

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