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Cancers
Special Issues
Deep Neural Networks for Cancer Screening and Classification
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Deep Neural Networks for Cancer Screening and Classification

A special issue of Cancers (ISSN 2072-6694).

Deadline for manuscript submissions: closed (1 July 2022) | Viewed by 8263

Special Issue Editors

Dr. Louis J. Vaickus

E-Mail Website
Guest Editor
Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA
Interests: digital pathology; digital histomorphometry; deep learning; artificial intelligence; whole-slide imaging; unsupervised models; semi-supervised models; loop machine learning; multimodal models
Dr. Joshua Levy

E-Mail Website
Guest Editor
Pathology and Dermatology, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA
Interests: machine learning; statistics; digital pathology; epidemiology; epigenetics

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue titled: Deep Neural Networks for Cancer Screening and Classification. Special consideration will be given to articles with the following features:

  1. Quantitative endpoints (e.g., NGS).
  2. Clinical validation of machine-learning tools.
  3. Multimodal modelling (e.g., combining two or more disparate data types).
  4. Applications in low-resource environments.
  5. Emerging model architectures (e.g., self-supervised, unsupervised, etc.).
  6. Multi-institution collaborations.

Dr. Louis J. Vaickus
Dr. Joshua Levy
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • deep learning
  • artificial intelligence
  • cancer classification
  • cancer screening
  • digital pathology
  • multimodal
  • semantic segmentation
  • unsupervised
  • semi-supervised

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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11 pages, 2217 KiB  
Article
Encoder-Weighted W-Net for Unsupervised Segmentation of Cervix Region in Colposcopy Images
by Jinhee Park, Hyunmo Yang, Hyun-Jin Roh, Woonggyu Jung and Gil-Jin Jang
Cancers 2022, 14(14), 3400; https://doi.org/10.3390/cancers14143400 - 13 Jul 2022
Cited by 8 | Viewed by 2017
Abstract
Cervical cancer can be prevented and treated better if it is diagnosed early. Colposcopy, a way of clinically looking at the cervix region, is an efficient method for cervical cancer screening and its early detection. The cervix region segmentation significantly affects the performance [...] Read more.
Cervical cancer can be prevented and treated better if it is diagnosed early. Colposcopy, a way of clinically looking at the cervix region, is an efficient method for cervical cancer screening and its early detection. The cervix region segmentation significantly affects the performance of computer-aided diagnostics using a colposcopy, particularly cervical intraepithelial neoplasia (CIN) classification. However, there are few studies of cervix segmentation in colposcopy, and no studies of fully unsupervised cervix region detection without image pre- and post-processing. In this study, we propose a deep learning-based unsupervised method to identify cervix regions without pre- and post-processing. A new loss function and a novel scheduling scheme for the baseline W-Net are proposed for fully unsupervised cervix region segmentation in colposcopy. The experimental results showed that the proposed method achieved the best performance in the cervix segmentation with a Dice coefficient of 0.71 with less computational cost. The proposed method produced cervix segmentation masks with more reduction in outliers and can be applied before CIN detection or other diagnoses to improve diagnostic performance. Our results demonstrate that the proposed method not only assists medical specialists in diagnosis in practical situations but also shows the potential of an unsupervised segmentation approach in colposcopy. Full article
(This article belongs to the Special Issue Deep Neural Networks for Cancer Screening and Classification)
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14 pages, 4477 KiB  
Article
Convolutional Neural Networks to Detect Vestibular Schwannomas on Single MRI Slices: A Feasibility Study
by Carole Koechli, Erwin Vu, Philipp Sager, Lukas Näf, Tim Fischer, Paul M. Putora, Felix Ehret, Christoph Fürweger, Christina Schröder, Robert Förster, Daniel R. Zwahlen, Alexander Muacevic and Paul Windisch
Cancers 2022, 14(9), 2069; https://doi.org/10.3390/cancers14092069 - 20 Apr 2022
Cited by 2 | Viewed by 2649
Abstract
In this study. we aimed to detect vestibular schwannomas (VSs) in individual magnetic resonance imaging (MRI) slices by using a 2D-CNN. A pretrained CNN (ResNet-34) was retrained and internally validated using contrast-enhanced T1-weighted (T1c) MRI slices from one institution. In a second step, [...] Read more.
In this study. we aimed to detect vestibular schwannomas (VSs) in individual magnetic resonance imaging (MRI) slices by using a 2D-CNN. A pretrained CNN (ResNet-34) was retrained and internally validated using contrast-enhanced T1-weighted (T1c) MRI slices from one institution. In a second step, the model was externally validated using T1c- and T1-weighted (T1) slices from a different institution. As a substitute, bisected slices were used with and without tumors originating from whole transversal slices that contained part of the unilateral VS. The model predictions were assessed based on the categorical accuracy and confusion matrices. A total of 539, 94, and 74 patients were included for training, internal validation, and external T1c validation, respectively. This resulted in an accuracy of 0.949 (95% CI 0.935–0.963) for the internal validation and 0.912 (95% CI 0.866–0.958) for the external T1c validation. We suggest that 2D-CNNs might be a promising alternative to 2.5-/3D-CNNs for certain tasks thanks to the decreased demand for computational power and the fact that there is no need for segmentations. However, further research is needed on the difference between 2D-CNNs and more complex architectures. Full article
(This article belongs to the Special Issue Deep Neural Networks for Cancer Screening and Classification)
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15 pages, 704 KiB  
Systematic Review
Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review
by Paul Windisch, Carole Koechli, Susanne Rogers, Christina Schröder, Robert Förster, Daniel R. Zwahlen and Stephan Bodis
Cancers 2022, 14(11), 2676; https://doi.org/10.3390/cancers14112676 - 27 May 2022
Cited by 12 | Viewed by 3002
Abstract
Objectives: To summarize the available literature on using machine learning (ML) for the detection and segmentation of benign tumors of the central nervous system (CNS) and to assess the adherence of published ML/diagnostic accuracy studies to best practice. Methods: The MEDLINE [...] Read more.
Objectives: To summarize the available literature on using machine learning (ML) for the detection and segmentation of benign tumors of the central nervous system (CNS) and to assess the adherence of published ML/diagnostic accuracy studies to best practice. Methods: The MEDLINE database was searched for the use of ML in patients with any benign tumor of the CNS, and the records were screened according to PRISMA guidelines. Results: Eleven retrospective studies focusing on meningioma (n = 4), vestibular schwannoma (n = 4), pituitary adenoma (n = 2) and spinal schwannoma (n = 1) were included. The majority of studies attempted segmentation. Links to repositories containing code were provided in two manuscripts, and no manuscripts shared imaging data. Only one study used an external test set, which raises the question as to whether some of the good performances that have been reported were caused by overfitting and may not generalize to data from other institutions. Conclusions: Using ML for detecting and segmenting benign brain tumors is still in its infancy. Stronger adherence to ML best practices could facilitate easier comparisons between studies and contribute to the development of models that are more likely to one day be used in clinical practice. Full article
(This article belongs to the Special Issue Deep Neural Networks for Cancer Screening and Classification)
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