Image modality
Pap,
H & E
IHC
H & E,
Deep contour-aware CNN Segmentation of colon
glands
GLAS challenge (165 images), MICCAI2015 nucleus segmentation
challenge (33 images)
F1-score,
object dice coefficient
40 tissue images of lung adenocarcinoma
(private set)
F1score,
Dice coefficient accuracy,
Deep learning architecture
for gastric cancer segmentation
Summary of articles using DLA for digital pathology image - Detection and classification of disease
Reference | Staining/image modality | Method | Application | Data set |
---|---|---|---|---|
Xu et al. 2016 [ ] | H&E | Stacked sparse autoencoders | Nucleus detection from Breast Cancer Histopathology Images | 537 H&E images from Case Western Reserve University |
Coudray et al. (2018) [ ] | H&E | Patch-based Inception-V3 model | Lung cancer histopathology images classify them into LUAD, LUSC, or normal lung tissue | FFPE sections (140 s) Frozen sections (98 s), and lung biopsies (102 s) |
Song et al. 2018 [ ] | H&E | Deep autoencoder | Simultaneous detection and classification of cells in bone marrow histology images | – |
Yi et al. 2018 [ ] | H&E | FCN | Microvessel prediction in H&E Stained Pathology Images | Lung adenocarcinoma (ADC) patients images 38 |
Bulten and Litjens 2018 [ ] | H&E, IHC | Self-clustering Convolutional adverse Arial Autoencoders | Classification of the pros take into tumor vs non-tumor | 94 registered WSIs from Radboud University Medical Center |
Valkonen et al. 2019 [ ] | ER, PR, Ki-67 | Fine-tuning partially pre-trained CNN network | Recognition of epithelial cells in breast cancers stained for ER, PR, and Ki-67 | Digital Pan CK (152 – invasive breast cancer images) |
Wei et al. 2019 [ ] | H&E | ResNet-18 based patch classifier | Classification of histologic subtypes on lung adenocarcinoma | 143 WSIs private set |
Wang et al. (2019) [ ] | H & E | Patch-based FCN and context-aware block selection + feature aggregation strategy | Lung cancer image classification | Private (939 WSIs), TCGA (500 WSIs) |
Li et al. 2019 [ ] | H & E | FCN trained with a concentric loss on weakly annotated centroid label | Mitosis detection in breast histopathology images | ICPR12 (50 images), ICPR14 (1696 images), AMIDA13 (606 images), TUPAC16 (107 images) |
Tabibu et al. .2019 [ ] | H & E | Pre-trained Res Net based patch classifier | Classification of Renal Cell Carcinoma subtypes and survival prediction | TCGA(2, 093WSI) |
Lin et al. 2019 [ ] | H & E | Fast Scan Net: FCN based model | Automatic detection of breast cancer metastases from whole-slide image | 2016 Camelyon Grand Challenge (400 WSI) |
NODE: Neural Ordinary Differential Equations; IoU: mean Intersection over Union coefficient
Endoscopy is the insertion of a long nonsurgical solid tube directly into the body for the visual examination of an internal organ or tissue in detail. Endoscopy is beneficial in studying several systems inside the human body, such as the gastrointestinal tract, the respiratory tract, the urinary tract, and the female reproductive tract [ 60 , 101 ]. Du et al. [ 31 ] reviewed the Applications of Deep Learning in the Analysis of Gastrointestinal Endoscopy Images. A revolutionary device for direct, painless, and non-invasive inspection of the gastrointestinal (GI) tract for detecting and diagnosing GI diseases (ulcer, bleeding) is Wireless capsule endoscopy (WCE). Soffer et al. [ 145 ] performed a systematic analysis of the existing literature on the implementation of deep learning in the WCE. The first deep learning-based framework was proposed by He et al. [ 46 ] for the detection of hookworm in WCE images. Two CNN networks integrated (edge extraction and classification of hookworm) to detect hookworm. Since tubular structures are crucial elements for hookworm detection, the edge extraction network was used for tubular region detection. Yoon et al. [ 185 ] developed a CNN model for early gastric cancer (EGC) identification and prediction of invasion depth. The depth of tumor invasion in early gastric cancer (EGC) is a significant factor in deciding the method of treatment. For the classification of endoscopic images as EGC or non-EGC, the authors employed a VGG-16 model. Nakagawa et al. [ 105 ] applied DL technique based on CNN to enhance the diagnostic assessment of oesophageal wall invasion using endoscopy. J.choi et al. [ 22 ] express the feature aspects of DL in endoscopy.
Positron Emission Tomography (PET) is a nuclear imaging tool that is generally used by the injection of particular radioactive tracers to visualize molecular-level activities within tissues. T. Wang et al. [ 168 ] reviewed applications of machine learning in PET attenuation correction (PET AC) and low-count PET reconstruction. The authors discussed the advantages of deep learning over machine learning in the applications of PET images. AJ reader et al. [ 123 ] reviewed the reconstruction of PET images that can be used in deep learning either directly or as a part of traditional reconstruction methods.
The primary purpose of this paper is to review numerous publications in the field of deep learning applications in medical images. Classification, detection, and segmentation are essential tasks in medical image processing [ 144 ]. For specific deep learning tasks in medical applications, the training of deep neural networks needs a lot of labeled data. But in the medical field, at least thousands of labeled data is not available. This issue is alleviated by a technique called transfer learning. Two transfer learning approaches are popular and widely applied that are fixed feature extractors and fine-tuning a pre-trained network. In the classification process, the deep learning models are used to classify images into two or more classes. In the detection process, Deep learning models have the function of identifying tumors and organs in medical images. In the segmentation task, deep learning models try to segment the region of interest in medical images for processing.
For medical image segmentation, deep learning has been widely used, and several articles have been published documenting the progress of deep learning in the area. Segmentation of breast tissue using deep learning alone has been successfully implemented [ 104 ]. Xing et al. [ 179 ] used CNN to acquire the initial shape of the nucleus and then isolate the actual nucleus using a deformable pattern. Qu et al. [ 118 ] suggested a deep learning approach that could segment the individual nucleus and classify it as a tumor, lymphocyte, and stroma nuclei. Pinckaers and Litjens [ 115 ] show on a colon gland segmentation dataset (GlaS) that these Neural Ordinary Differential Equations (NODE) can be used within the U-Net framework to get better segmentation results. Sun 2019 [ 149 ] developed a deep learning architecture for gastric cancer segmentation that shows the advantage of utilizing multi-scale modules and specific convolution operations together. Figure Figure6 6 shows U-Net is the most usually used network for segmentation (Fig. (Fig.6 6 ).
U-Net architecture for segmentation,comprising encoder (downsampling) and decoder (upsampling) sections [ 135 ]
The main challenge posed by methods of detection of lesions is that they can give rise to multiple false positives while lacking a good proportion of true positive ones . For tuberculosis detection using deep learning methods applied in [ 53 , 57 , 58 , 91 , 119 ]. Pulmonary nodule detection using deep learning has been successfully applied in [ 82 , 108 , 136 , 157 ].
Shin et al. [ 141 ] discussed the effect of CNN pre-trained architectures and transfer learning on the identification of enlarged thoracoabdominal lymph nodes and the diagnosis of interstitial lung disease on CT scans, and considered transfer learning to be helpful, given the fact that natural images vary from medical images. Litjens et al. [ 85 ] introduced CNN for the identification of Prostate cancer in biopsy specimens and breast cancer metastasis identification in sentinel lymph nodes. The CNN has four convolution layers for feature extraction and three classification layers. Riddle et al. [ 124 ] proposed the Faster R-CNN model for the detection of mammography lesions and classified these lesions into benign and malignant, which finished second in the Digital Mammography DREAM Challenge. Figure Figure7 7 shows VGG architecture for detection.
CNN architecture for detection [ 144 ]
An object detection framework named Clustering CNN (CLU-CNNs) was proposed by Z. Li et al. [ 76 ] for medical images. CLU-CNNs used Agglomerative Nesting Clustering Filtering (ANCF) and BN-IN Net to avoid much computation cost facing medical images. Image saliency detection aims at locating the most eye-catching regions in a given scene [ 21 , 78 ]. The goal of image saliency detection is to locate a given scene in the most eye-catching regions. In different applications, it also acts as a pre-processing tool including video saliency detection [ 17 , 18 ], object recognition, and object tracking [ 20 ]. Saliency maps are a commonly used tool for determining which areas are most important to the prediction of a trained CNN on the input image [ 92 ]. NT Arun et al. [ 4 ] evaluated the performance of several popular saliency methods on the RSNA Pneumonia Detection dataset and was found that GradCAM was sensitive to the model parameters and model architecture.
In classification tasks, deep learning techniques based on CNN have seen several advancements. The success of CNN in image classification has led researchers to investigate its usefulness as a diagnostic method for identifying and characterizing pulmonary nodules in CT images. The classification of lung nodules using deep learning [ 74 , 108 , 117 , 141 ] has also been successfully implemented.
Breast parenchymal density is an important indicator of the risk of breast cancer. The DL algorithms used for density assessment can significantly reduce the burden of the radiologist. Breast density classification using DL has been successfully implemented [ 37 , 59 , 72 , 177 ]. Ionescu et al. [ 59 ] introduced a CNN-based method to predict Visual Analog Score (VAS) for breast density estimation. Figure Figure8 8 shows AlexNet architecture for classification.
CNN architecture for classification [ 144 ]
Alcoholism or alcohol use disorder (AUD) has effects on the brain. The structure of the brain was observed using the Neuroimaging approach. S.H.Wang et al. [ 162 ] proposed a 10-layer CNN for alcohol use disorder (AUD) problem using dropout, batch normalization, and PReLU techniques. The authors proposed a 10 layer CNN model that has obtained a sensitivity of 97.73, a specificity of 97.69, and an accuracy of 97.71. Cerebral micro-bleeding (CMB) are small chronic brain hemorrhages that can result in cognitive impairment, long-term disability, and neurologic dysfunction. Therefore, early-stage identification of CMBs for prompt treatment is essential. S. Wang et al. [ 164 ] proposed the transfer learning-based DenseNet to detect Cerebral micro-bleedings (CMBs). DenseNet based model attained an accuracy of 97.71% (Fig. (Fig.8 8 ).
The application of deep learning algorithms to medical imaging is fascinating, but many challenges are pulling down the progress. One of the limitations to the adoption of DL in medical image analysis is the inconsistency in the data itself (resolution, contrast, signal-to-noise), typically caused by procedures in clinical practice [ 113 ]. The non-standardized acquisition of medical images is another limitation in medical image analysis. The need for comprehensive medical image annotations limits the applicability of deep learning in medical image analysis. The major challenge is limited data and compared to other datasets, the sharing of medical data is incredibly complicated. Medical data privacy is both a sociological and a technological issue that needs to be discussed from both viewpoints. For building DLA a large amount of annotated data is required. Annotating medical images is another major challenge. Labeling medical images require radiologists’ domain knowledge. Therefore, it is time-consuming to annotate adequate medical data. Semi-supervised learning could be implemented to make combined use of the existing labeled data and vast unlabelled data to alleviate the issue of “limited labeled data”. Another way to resolve the issue of “data scarcity” is to develop few-shot learning algorithms using a considerably smaller amount of data. Despite the successes of DL technology, there are many restrictions and obstacles in the medical field. Whether it is possible to reduce medical costs, increase medical efficiency, and improve the satisfaction of patients using DL in the medical field cannot be adequately checked. However, in clinical trials, it is necessary to demonstrate the efficacy of deep learning methods and to develop guidelines for the medical image analysis applications of deep learning.
Medical imaging is a place of origin of the information necessary for clinical decisions. This paper discusses the new algorithms and strategies in the area of deep learning. In this brief introduction to DLA in medical image analysis, there are two objectives. The first one is an introduction to the field of deep learning and the associated theory. The second is to provide a general overview of the medical image analysis using DLA. It began with the history of neural networks since 1940 and ended with breakthroughs in medical applications in recent DL algorithms. Several supervised and unsupervised DL algorithms are first discussed, including auto-encoders, recurrent, CNN, and restricted Boltzmann machines. Several optimization techniques and frameworks in this area include Caffe, TensorFlow, Theano, and PyTorch are discussed. After that, the most successful DL methods were reviewed in various medical image applications, including classification, detection, and segmentation. Applications of the RBM network is rarely published in the medical image analysis literature. In classification and detection, CNN-based models have achieved good results and are most commonly used. Several existing solutions to medical challenges are available. However, there are still several issues in medical image processing that need to be addressed with deep learning. Many of the current DL implementations are supervised algorithms, while deep learning is slowly moving to unsupervised and semi-supervised learning to manage real-world data without manual human labels.
DLA can support clinical decisions for next-generation radiologists. DLA can automate radiologist workflow and facilitate decision-making for inexperienced radiologists. DLA is intended to aid physicians by automatically identifying and classifying lesions to provide a more precise diagnosis. DLA can help physicians to minimize medical errors and increase medical efficiency in the processing of medical image analysis. DL-based automated diagnostic results using medical images for patient treatment are widely used in the next few decades. Therefore, physicians and scientists should seek the best ways to provide better care to the patient with the help of DLA. The potential future research for medical image analysis is the designing of deep neural network architectures using deep learning. The enhancement of the design of network structures has a direct impact on medical image analysis. Manual design of DL Model structure requires rich knowledge; hence Neural Network Search will probably replace the manual design [ 73 ]. A meaningful feature research direction is also the design of various activation functions. Radiation therapy is crucial for cancer treatment. Different medical imaging modalities are playing a critical role in treatment planning. Radiomics was defined as the extraction of high throughput features from medical images [ 28 ]. In the feature, Deep-learning analysis of radionics will be a promising tool in clinical research for clinical diagnosis, drug development, and treatment selection for cancer patients . Due to limited annotated medical data, unsupervised, weakly supervised, and reinforcement learning methods are the emerging research areas in DL for medical image analysis. Overall, deep learning, a new and fast-growing field, offers various obstacles as well as opportunities and solutions for a range of medical image applications.
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With the rapid development of artificial intelligence technology, deep learning is being applied to the field of medical image analysis. This paper summarizes the deep learning models related to medical image analysis, and the application results of these models in medical image classification, detection, segmentation and registration. It specifically involves the image analysis tasks of nerve, retina, lung, digital pathology, breast, musculoskeletal and other aspects. Finally, it summarizes the current research status of deep learning related to medical image analysis, and discusses the challenges and direction of future research.
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An, Q., Jiang, B., Yuan, J. (2022). The Overview of Medical Image Processing Based on Deep Learning. In: Su, R., Zhang, YD., Liu, H. (eds) Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2021). MICAD 2021. Lecture Notes in Electrical Engineering, vol 784. Springer, Singapore. https://doi.org/10.1007/978-981-16-3880-0_43
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Research on defect diagnosis of transmission lines based on multi-strategy image processing and improved deep network.
2. multi-strategy image processing, 2.1. image enhancement based on wavelet denoising, 2.2. multi-threshold segmentation based on hsv color space, 2.3. extraction of transmission line regions based on morphological processing, 3. detection of transmission line defects, 3.2. googlenet, 3.3. focal loss, 3.4. proposed method, 4. experimental results and analysis, 4.1. dataset introduction, 4.2. results and analysis, 5. conclusions, author contributions, data availability statement, conflicts of interest.
Click here to enlarge figure
Defect Class | Normal | Loose | Broken |
---|---|---|---|
Train | 600 | 586 | 294 |
Test | 60 | 60 | 60 |
Label | 0 | 1 | 2 |
Method | Accuracy (%) | Recall (%) | F1-Score (%) | Loss |
---|---|---|---|---|
AlexNet | 86.34 | 90.11 | 89.10 | 0.20 |
DenseNet | 94.01 | 97.20 | 97.37 | 0.16 |
MobileNet-V2 | 91.76 | 96.97 | 96.68 | 0.19 |
Proposed method | 97.83 | 97.81 | 97.78 | 0.04 |
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Gou, M.; Tang, H.; Song, L.; Chen, Z.; Yan, X.; Zeng, X.; Fu, W. Research on Defect Diagnosis of Transmission Lines Based on Multi-Strategy Image Processing and Improved Deep Network. Processes 2024 , 12 , 1832. https://doi.org/10.3390/pr12091832
Gou M, Tang H, Song L, Chen Z, Yan X, Zeng X, Fu W. Research on Defect Diagnosis of Transmission Lines Based on Multi-Strategy Image Processing and Improved Deep Network. Processes . 2024; 12(9):1832. https://doi.org/10.3390/pr12091832
Gou, Ming, Hao Tang, Lei Song, Zhong Chen, Xiaoming Yan, Xiangwen Zeng, and Wenlong Fu. 2024. "Research on Defect Diagnosis of Transmission Lines Based on Multi-Strategy Image Processing and Improved Deep Network" Processes 12, no. 9: 1832. https://doi.org/10.3390/pr12091832
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