Exploiting the potential of unlabeled endoscopic video data with self-supervised learning

Roß, Tobias; Zabala-Blanco, David; Vemuri, Anant; Isensee, Fabian; Wiesenfarth, Manuel; Bodenstedt, Sebastian; Both, Fabian; Kessler, Philip; Wagner, Martin; Müller, Beat; Kenngott, Hannes; Speidel, Stefanie; Kopp‐Schneider, Annette; Maier‐Hein, Klaus H.; Maier-Hein, Lena

doi:10.1007/s11548-018-1772-0

Cited by 126 publications

(91 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An approach that is gaining popularity is finding a representation from which (part of) the data can be reconstructed. For example, Ross et al (2017) first decolorize their training images, then use recolorization as an additional task to learn a good representation.…”

Section: Reference Topicmentioning

confidence: 99%

Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis

Cheplygina

Bruijne

Pluim

2019

Medical Image Analysis

723

441

View full text Add to dashboard Cite

Machine learning (ML) algorithms have made a tremendous impact in the field of medical imaging. While medical imaging datasets have been growing in size, a challenge for supervised ML algorithms that is frequently mentioned is the lack of annotated data. As a result, various methods that can learn with less/other types of supervision, have been proposed. We give an overview of semi-supervised, multiple instance, and transfer learning in medical imaging, both in diagnosis or segmentation tasks. We also discuss connections between these learning scenarios, and opportunities for future research.

show abstract

Section: Reference Topicmentioning

confidence: 99%

Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis

Cheplygina

Bruijne

Pluim

2019

Medical Image Analysis

723

441

View full text Add to dashboard Cite

show abstract

“…Specifically, self-supervised pre-training consists of assigning surrogate or proxy labels to the unlabeled data and then training a randomly initialized network using the resulting surrogate supervision signal. The advantage of model pre-training using unlabeled medical data is that the learned knowledge is related to the target medical task; and thus, can be more effective than transfer learning from a foregin domain (e.g., Tajbakhsh et al (2019) and Ross et al (2018)).…”

Section: Self-supervised Pre-trainingmentioning

confidence: 99%

Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation

Tajbakhsh¹,

Jeyaseelan²,

Li³

et al. 2020

Medical Image Analysis

678

358

View full text Add to dashboard Cite

The medical imaging literature has witnessed remarkable progress in high-performing segmentation models based on convolutional neural networks. Despite the new performance highs, the recent advanced segmentation models still require large, representative, and high quality annotated datasets. However, rarely do we have a perfect training dataset, particularly in the field of medical imaging, where data and annotations are both expensive to acquire. Recently, a large body of research has studied the problem of medical image segmentation with imperfect datasets, tackling two major dataset limitations: scarce annotations where only limited annotated data is available for training, and weak annotations where the training data has only sparse annotations, noisy annotations, or image-level annotations. In this article, we provide a detailed review of the solutions above, summarizing both the technical novelties and empirical results. We further compare the benefits and requirements of the surveyed methodologies and provide our recommended solutions to the problems of scarce and weak annotations. We hope this review increases the community awareness of the techniques to handle imperfect datasets.

show abstract

“…It is very complex to create a completely automatic system, so that a reasonable choice in this kind of scenarios is to create a semi-supervised approach. On the one hand, this strategy can be checked in the literature to be a suitable approach to big data problems in emerging areas, in which data quality is revealed in those early stages to have an even more-than-suspected potential high impact on the company [ 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 ]. On the other hand, the Information Technology areas or similar ones often have staff partially devoted and responsible for the data quality aspects.…”

Section: Discussionmentioning

confidence: 99%

Entropic Statistical Description of Big Data Quality in Hotel Customer Relationship Management

Serrano

Ballestero

Muñoz-Romero

et al. 2019

Entropy

View full text Add to dashboard Cite

Customer Relationship Management (CRM) is a fundamental tool in the hospitality industry nowadays, which can be seen as a big-data scenario due to the large amount of recordings which are annually handled by managers. Data quality is crucial for the success of these systems, and one of the main issues to be solved by businesses in general and by hospitality businesses in particular in this setting is the identification of duplicated customers, which has not received much attention in recent literature, probably and partly because it is not an easy-to-state problem in statistical terms. In the present work, we address the problem statement of duplicated customer identification as a large-scale data analysis, and we propose and benchmark a general-purpose solution for it. Our system consists of four basic elements: (a) A generic feature representation for the customer fields in a simple table-shape database; (b) An efficient distance for comparison among feature values, in terms of the Wagner-Fischer algorithm to calculate the Levenshtein distance; (c) A big-data implementation using basic map-reduce techniques to readily support the comparison of strategies; (d) An X-from-M criterion to identify those possible neighbors to a duplicated-customer candidate. We analyze the mass density function of the distances in the CRM text-based fields and characterized their behavior and consistency in terms of the entropy and of the mutual information for these fields. Our experiments in a large CRM from a multinational hospitality chain show that the distance distributions are statistically consistent for each feature, and that neighbourhood thresholds are automatically adjusted by the system at a first step and they can be subsequently more-finely tuned according to the manager experience. The entropy distributions for the different variables, as well as the mutual information between pairs, are characterized by multimodal profiles, where a wide gap between close and far fields is often present. This motivates the proposal of the so-called X-from-M strategy, which is shown to be computationally affordable, and can provide the expert with a reduced number of duplicated candidates to supervise, with low X values being enough to warrant the sensitivity required at the automatic detection stage. The proposed system again encourages and supports the benefits of big-data technologies in CRM scenarios for hotel chains, and rather than the use of ad-hoc heuristic rules, it promotes the research and development of theoretically principled approaches.

show abstract

Exploiting the potential of unlabeled endoscopic video data with self-supervised learning

Abstract: As it makes efficient use of available (non-)public and (un-)labeled data, the approach has the potential to become a valuable tool for CNN (pre-)training.

Cited by 126 publications

References 31 publications

Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis

Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis

Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation

Entropic Statistical Description of Big Data Quality in Hotel Customer Relationship Management

Contact Info

Product

Resources

About