Survey of Recent Deep Neural Networks with Strong Annotated Supervision in Histopathology
Deep learning (DL) and convolutional neural networks (CNNs) have achieved state-of-the-art performance in many medical image analysis tasks. Histopathological images contain valuable information that can be used to diagnose diseases and create treatment plans. Therefore, the application of DL for th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Computation |
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| Online Access: | https://www.mdpi.com/2079-3197/11/4/81 |
| _version_ | 1797605892138467328 |
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| author | Dominika Petríková Ivan Cimrák |
| author_facet | Dominika Petríková Ivan Cimrák |
| author_sort | Dominika Petríková |
| collection | DOAJ |
| description | Deep learning (DL) and convolutional neural networks (CNNs) have achieved state-of-the-art performance in many medical image analysis tasks. Histopathological images contain valuable information that can be used to diagnose diseases and create treatment plans. Therefore, the application of DL for the classification of histological images is a rapidly expanding field of research. The popularity of CNNs has led to a rapid growth in the number of works related to CNNs in histopathology. This paper aims to provide a clear overview for better navigation. In this paper, recent DL-based classification studies in histopathology using strongly annotated data have been reviewed. All the works have been categorized from two points of view. First, the studies have been categorized into three groups according to the training approach and model construction: 1. fine-tuning of pre-trained networks for one-stage classification, 2. training networks from scratch for one-stage classification, and 3. multi-stage classification. Second, the papers summarized in this study cover a wide range of applications (e.g., breast, lung, colon, brain, kidney). To help navigate through the studies, the classification of reviewed works into tissue classification, tissue grading, and biomarker identification was used. |
| first_indexed | 2024-03-11T05:07:30Z |
| format | Article |
| id | doaj.art-df72631d86d64811855ac6f22fc9afc3 |
| institution | Directory Open Access Journal |
| issn | 2079-3197 |
| language | English |
| last_indexed | 2024-03-11T05:07:30Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computation |
| spelling | doaj.art-df72631d86d64811855ac6f22fc9afc32023-11-17T18:49:12ZengMDPI AGComputation2079-31972023-04-011148110.3390/computation11040081Survey of Recent Deep Neural Networks with Strong Annotated Supervision in HistopathologyDominika Petríková0Ivan Cimrák1Cell-in-Fluid Biomedical Modelling & Computations Group, Faculty of Management Science and Informatics, University of Žilina, Univerzitná 8215/1, 010 26 Žilina, SlovakiaCell-in-Fluid Biomedical Modelling & Computations Group, Faculty of Management Science and Informatics, University of Žilina, Univerzitná 8215/1, 010 26 Žilina, SlovakiaDeep learning (DL) and convolutional neural networks (CNNs) have achieved state-of-the-art performance in many medical image analysis tasks. Histopathological images contain valuable information that can be used to diagnose diseases and create treatment plans. Therefore, the application of DL for the classification of histological images is a rapidly expanding field of research. The popularity of CNNs has led to a rapid growth in the number of works related to CNNs in histopathology. This paper aims to provide a clear overview for better navigation. In this paper, recent DL-based classification studies in histopathology using strongly annotated data have been reviewed. All the works have been categorized from two points of view. First, the studies have been categorized into three groups according to the training approach and model construction: 1. fine-tuning of pre-trained networks for one-stage classification, 2. training networks from scratch for one-stage classification, and 3. multi-stage classification. Second, the papers summarized in this study cover a wide range of applications (e.g., breast, lung, colon, brain, kidney). To help navigate through the studies, the classification of reviewed works into tissue classification, tissue grading, and biomarker identification was used.https://www.mdpi.com/2079-3197/11/4/81classificationconvolutional neural networksdeep learningdigital pathologyhistology image analysis |
| spellingShingle | Dominika Petríková Ivan Cimrák Survey of Recent Deep Neural Networks with Strong Annotated Supervision in Histopathology Computation classification convolutional neural networks deep learning digital pathology histology image analysis |
| title | Survey of Recent Deep Neural Networks with Strong Annotated Supervision in Histopathology |
| title_full | Survey of Recent Deep Neural Networks with Strong Annotated Supervision in Histopathology |
| title_fullStr | Survey of Recent Deep Neural Networks with Strong Annotated Supervision in Histopathology |
| title_full_unstemmed | Survey of Recent Deep Neural Networks with Strong Annotated Supervision in Histopathology |
| title_short | Survey of Recent Deep Neural Networks with Strong Annotated Supervision in Histopathology |
| title_sort | survey of recent deep neural networks with strong annotated supervision in histopathology |
| topic | classification convolutional neural networks deep learning digital pathology histology image analysis |
| url | https://www.mdpi.com/2079-3197/11/4/81 |
| work_keys_str_mv | AT dominikapetrikova surveyofrecentdeepneuralnetworkswithstrongannotatedsupervisioninhistopathology AT ivancimrak surveyofrecentdeepneuralnetworkswithstrongannotatedsupervisioninhistopathology |