A review on deep learning in medical image analysis

Suganyadevi, S.; Seethalakshmi, V.; Balasamy, K.

doi:10.1007/s13735-021-00218-1

Cited by 261 publications

(112 citation statements)

References 114 publications

(95 reference statements)

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“…AI-based automatic learning and diagnosis models could easily solve complex problems during medical image analysis (8). Deep learning (DL) and machine learning could promote further optimization of the AI-based image-processing models (9). DL methods have shown great advantages in image identification and classification (10), especially in cell classification (11), cancer detection (12), pathological diagnosis (13), and characterization of the spatial organization of immune cells in the TME (14).…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Assisted Score Analysis for Predicting the Expression of the Immunotherapy Biomarker PD-L1 in Lung Cancer

et al. 2022

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Programmed cell death ligand 1 (PD-L1) is a critical biomarker for predicting the response to immunotherapy. However, traditional quantitative evaluation of PD-L1 expression using immunohistochemistry staining remains challenging for pathologists. Here we developed a deep learning (DL)-based artificial intelligence (AI) model to automatically analyze the immunohistochemical expression of PD-L1 in lung cancer patients. A total of 1,288 patients with lung cancer were included in the study. The diagnostic ability of three different AI models (M1, M2, and M3) was assessed in both PD-L1 (22C3) and PD-L1 (SP263) assays. M2 and M3 showed improved performance in the evaluation of PD-L1 expression in the PD-L1 (22C3) assay, especially at 1% cutoff. Highly accurate performance in the PD-L1 (SP263) was also achieved, with accuracy and specificity of 96.4 and 96.8% in both M2 and M3, respectively. Moreover, the diagnostic results of these three AI-assisted models were highly consistent with those from the pathologist. Similar performances of M1, M2, and M3 in the 22C3 dataset were also obtained in lung adenocarcinoma and lung squamous cell carcinoma in both sampling methods. In conclusion, these results suggest that AI-assisted diagnostic models in PD-L1 expression are a promising tool for improving the efficiency of clinical pathologists.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Assisted Score Analysis for Predicting the Expression of the Immunotherapy Biomarker PD-L1 in Lung Cancer

et al. 2022

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“…However, we only tune the original object detector using the negative set x t S with no positive samples to detect. Therefore, if the original detector finds a person bounding box to be considered a false positive 2 Since the consecutive frames of a surveillance video are almost identical, it is better to extract just one frame as a representative sample than to sample all the frames.…”

Section: Context-aware Domain Adaptation For Intelligent Back-endmentioning

confidence: 99%

“…Recent successful technological advances in deep learningbased image/video recognition haves led to an explosion in consumer demand for Artificial Intelligence (AI)-based products and services in many real-world applications such as autonomous driving [1], medical image processing [2], virtual training simulator [3] and video surveillance [4]. Among these applications, much attention has been paid to intelligent video surveillance based on AI to ensure rapid and precise exchange of safety information.…”

Section: Introductionmentioning

confidence: 99%

Toward an Online Continual Learning Architecture for Intrusion Detection of Video Surveillance

Kwon

Kim

2022

IEEE Access

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With recent advances in deep learning technologies, many commercialized video surveillance systems have adopted Artificial Intelligence (AI)-powered video analytics technologies as a way to make our life smarter and safer. Nevertheless, there is no robust architecture with an appropriate network model for commercial services considering both high accuracy and low computational cost. Existing deep learning technologies would not be enough to model and represent the dynamics of the real-world scene, so it is difficult to satisfy all environments using a generic model. Appropriate training data from false-alarm and/or missed cases can address this limitation but is rarely available due to legal issues relating to the privacy of personal data and the unpredictability of new incoming data. In this paper, we propose a novel end-to-end hybrid video surveillance architecture for reliable object detection, consisting of front-end and back-end intelligence. For the intelligent front-end, we propose a new object detector with a Multi-scale ResBlock scheme to consider the scalability and flexibility of the system. We are also developing a new domain adaptation method to replace the generic model with each camera's individual personal model by understanding real-time space and context information for intelligent back-end architecture. It is an iterative and continuous process in which new upcoming data and previous models are consistently engaged in a continuous improvement process. We conducted a series of experiments, including an interesting proof-ofconcept tests called the Chameleon project, which demonstrated the high accuracy and versatility of the new architecture, while producing robust results that can be implemented in practice.

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“…Current advancements in AI, especially regarding deep gaining knowledge of techniques, are helping in identification, classification, and quantification styles in medical image (Suganyadevi et al, 2022). Moreover, it is the fastest growing subject in synthetic intelligence and it's efficaciously applied currently in several areas, inclusive of medication.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning and Computer Vision: Machine Learning Analysis and Image Processing of Puromycin Treated Microscopy

Owoeye¹,

Ojo-Omoniyi²

2022

Int.J.Curr.Microbiol.App.Sci

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Digital image processing involves the usage of a functional algorithm to process images with special regions of interest. In most case scenarios, this is termed as an active aspect of digital signal processing; image processing comes with several rewards over analog image processing. Its relevance and application spans Autonomous Vehicles, Biometric fingerprint technologies as well as Face recognition applications. Reliable statistics through feature engineering from the image can be extracted and in turn serve as focus points of deep learning insights. Besides, its application in monitoring Climatic changes, Agricultural crop yields, security measures, industrial manufacturing as well as medical fields exponentially advances each day. Meanwhile, deep learning being a feature of Artificial Intelligence has brought forward several useful models that is being used as transfer base for further model accuracies and baselines. In this study, we make use of a certain Microscopy datasets, sampling one of the images for digital processing, in order to gain useful insights through Cropped Quantizing, Laplace Edge Detection and Gaussian noise with sigma methods respectively. The statistical results of the extracted image features through Support Vector Method (SVM) give accuracy of up to 75%.

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A review on deep learning in medical image analysis

Cited by 261 publications

References 114 publications

Artificial Intelligence-Assisted Score Analysis for Predicting the Expression of the Immunotherapy Biomarker PD-L1 in Lung Cancer

Artificial Intelligence-Assisted Score Analysis for Predicting the Expression of the Immunotherapy Biomarker PD-L1 in Lung Cancer

Toward an Online Continual Learning Architecture for Intrusion Detection of Video Surveillance

Deep Learning and Computer Vision: Machine Learning Analysis and Image Processing of Puromycin Treated Microscopy

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