Automatic Disease Detection and Report Generation for Gastrointestinal Tract Examination

Harzig, Philipp; Einfalt, Moritz; Lienhart, Rainer

doi:10.1145/3343031.3356066

Cited by 13 publications

(13 citation statements)

References 11 publications

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“…We also identify a group of papers [46,48,74,92,95] following a Template based approach. The language component in these works operates programmatically by following if-then rules or other heuristics in order to retrieve, fill and/or combine templates from a database in order to generate a report.…”

Section: Language Componentmentioning

confidence: 99%

“…The visual component typically outputs discrete classification labels that the language component processes programmatically. In the case of Harzig et al 2019b [48], image localizations per class are also recovered using CAM [157], and in the case of Han et al [46] the visual component outputs an image segmentation. In both cases the language component includes special localization-based rules or templates, thus incorporating location information in the generated report.…”

Section: Language Componentmentioning

confidence: 99%

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A Survey on Deep Learning and Explainability for Automatic Report Generation from Medical Images

Messina¹,

Pino²,

Parra³

et al. 2020

Preprint

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Every year physicians face an increasing demand of image-based diagnosis from patients, a problem that can be addressed with recent artificial intelligence methods. In this context, we survey works in the area of automatic report generation from medical images, with emphasis on methods using deep neural networks, with respect to: (1) Datasets, (2) Architecture Design, (3) Explainability and (4) Evaluation Metrics. Our survey identifies interesting developments, but also remaining challenges. Among them, the current evaluation of generated reports is especially weak, since it mostly relies on traditional Natural Language Processing (NLP) metrics, which do not accurately capture medical correctness.

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Section: Language Componentmentioning

confidence: 99%

Section: Language Componentmentioning

confidence: 99%

A Survey on Deep Learning and Explainability for Automatic Report Generation from Medical Images

Messina¹,

Pino²,

Parra³

et al. 2020

Preprint

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“…Both sides of the pylorus must be examined to detect abnormalities like ulcer or erosion [ 5 , 7 ]. With screening GI tract, the physician can ensure that the pylorus can control the motion of food by condensing muscles [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

“…Automatic detection of anatomical landmark has been considered in many previous studies [ 4 , 8 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Proposing Novel Data Analytics Method for Anatomical Landmark Identification from Endoscopic Video Frames

Nezhad

Khatibi

Sohrabi

2022

Journal of Healthcare Engineering

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Background. The anatomical landmarks contain the characteristics that are used to guide the gastroenterologists during the endoscopy. The expert can also ensure the completion of examination with the help of the anatomical landmarks. Automatic detection of anatomical landmarks in endoscopic video frames can be helpful for guiding the physicians during screening the gastrointestinal tract (GI). Method. This study presents an automatic novel method for anatomical landmark detection of GI tract from endoscopic video frames based on semisupervised deep convolutional neural network (CNN) and compares the results with supervised CNN model. We consider the anatomical landmarks from Kvasir dataset that includes 500 images for each class of Z-line, pylorus, and cecum. The resolution of these images varies from 750 × 576 up to 1920 × 1072 pixels. Result. Experimental results show that the supervised CNN has highly desirable performance with accuracy of 100%. Also, our proposed semisupervised CNN can compete with a slight difference similar to the CNN model. Our proposed semisupervised model trained using 1, 5, 10, and 20 percent of training data records as labeled training dataset has the average accuracy of 83%, 98%, 99%, and 99%, respectively. Conclusion. The main advantage of our proposed method is achieving the high accuracy with small amount of labeled data without spending time for labeling more data. The strength of our proposed method saves the required labor, cost, and time for data labeling.

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“…The dataset was later extended to 8,000 images. Using Kvasir, researchers all over the world have started developing different ML models and AI systems for GI endoscopy [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38] . Moreover, the Kvasir dataset has been used to organize international competitions, i.e., the Medico Task at MediaEval in 2017 39 and 2018 40 and the ACM Multimedia 2019 BioMedia Grand Challenge 41 .…”

mentioning

confidence: 99%

HyperKvasir, A Comprehensive Multi-Class Image and Video Dataset for Gastrointestinal Endoscopy

Borgli¹,

Thambawita²,

Smedsrud³

et al. 2019

Preprint

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Artificial intelligence is currently a hot topic in medicine. The fact that medical data is often sparse and hard to obtain due to legal restrictions and lack of medical personnel to perform the cumbersome and tedious labeling of the data leads to limitations for what would be possible to achieve with automatic analysis. In this respect, this article presents HyperKvasir which is the largest image and video dataset of the gastrointestinal tract available today. The data is collected during real gastro- and colonoscopy examinations at Bærum Hospital in Norway and partly labeled by experienced gastrointestinal endoscopists. The dataset contains 110,079 images and 373 videos and represents anatomical landmarks and pathological and normal findings. The total number of images and video frames together is around 1,17 million. Initial experiments demonstrate the potential benefits of artificial intelligence-based computer assisted diagnosis systems. The HyperKvasir dataset can play an important role in developing better algorithms and computer assisted examination systems not just for gastro- and colonoscopy, butpossible also other fields in medicine.

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Automatic Disease Detection and Report Generation for Gastrointestinal Tract Examination

Cited by 13 publications

References 11 publications

A Survey on Deep Learning and Explainability for Automatic Report Generation from Medical Images

A Survey on Deep Learning and Explainability for Automatic Report Generation from Medical Images

Proposing Novel Data Analytics Method for Anatomical Landmark Identification from Endoscopic Video Frames

HyperKvasir, A Comprehensive Multi-Class Image and Video Dataset for Gastrointestinal Endoscopy

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