A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Experiences from the ImageCLEF Medical Retrieval and Annotation Tasks

The medical tasks in ImageCLEF have been run every year from 2004-2018 and many different tasks and data sets have been used over these years. The created resources are being used by many researchers well beyond the actual evaluation campaigns and are allowing to compare the performance of many techniques on the same grounds and in a reproducible way. Many of the larger data sets are from the medical literature, as such images are easier to obtain and to share than clinical data, which was used in a few smaller ImageCLEF challenges that are specifically marked with the disease type and anatomic region. This chapter describes the main results of the various tasks over the years, including data, participants, types of tasks evaluated and also the lessons learned in organizing such tasks for the scientific community

Morphological Profiling for Drug Discovery in the Era of Deep Learning

Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features of cells or organisms in response to perturbations at the single-cell resolution. Concurrently, significant advances in machine learning and deep learning, especially in computer vision, have led to substantial improvements in analyzing large-scale high-content images at high-throughput. These efforts have facilitated understanding of compound mechanism-of-action (MOA), drug repurposing, characterization of cell morphodynamics under perturbation, and ultimately contributing to the development of novel therapeutics. In this review, we provide a comprehensive overview of the recent advances in the field of morphological profiling. We summarize the image profiling analysis workflow, survey a broad spectrum of analysis strategies encompassing feature engineering- and deep learning-based approaches, and introduce publicly available benchmark datasets. We place a particular emphasis on the application of deep learning in this pipeline, covering cell segmentation, image representation learning, and multimodal learning. Additionally, we illuminate the application of morphological profiling in phenotypic drug discovery and highlight potential challenges and opportunities in this field.Comment: 44 pages, 5 figure, 5 table

Modified Firefly Optimization with Deep Learning based Multimodal Biometric Verification Model

Biometric security has become a main concern in the data security field. Over the years, initiatives in the biometrics field had an increasing growth rate. The multimodal biometric method with greater recognition and precision rate for smart cities remains to be a challenge. By comparison, made with the single biometric recognition, we considered the multimodal biometric recognition related to finger vein and fingerprint since it has high security, accurate recognition, and convenient sample collection. This article presents a Modified Firefly Optimization with Deep Learning based Multimodal Biometric Verification (MFFODL-MBV) model. The presented MFFODL-MBV technique performs biometric verification using multiple biometrics such as fingerprint, DNA, and microarray. In the presented MFFODL-MBV technique, EfficientNet model is employed for feature extraction. For biometric recognition, MFFO algorithm with long short-term memory (LSTM) model is applied with MFFO algorithm as hyperparameter optimizer. To ensure the improved outcomes of the MFFODL-MBV approach, a widespread experimental analysis was performed. The wide-ranging experimental analysis reported improvements in the MFFODL-MBV technique over other models

Pragmatic Evaluation of Health Monitoring & Analysis Models from an Empirical Perspective

Implementing and deploying several linked modules that can conduct real-time analysis and recommendation of patient datasets is necessary for designing health monitoring and analysis models. These databases include, but are not limited to, blood test results, computer tomography (CT) scans, MRI scans, PET scans, and other imaging tests. A combination of signal processing and image processing methods are used to process them. These methods include data collection, pre-processing, feature extraction and selection, classification, and context-specific post-processing. Researchers have put forward a variety of machine learning (ML) and deep learning (DL) techniques to carry out these tasks, which help with the high-accuracy categorization of these datasets. However, the internal operational features and the quantitative and qualitative performance indicators of each of these models differ. These models also demonstrate various functional subtleties, contextual benefits, application-specific constraints, and deployment-specific future research directions. It is difficult for researchers to pinpoint models that perform well for their application-specific use cases because of the vast range of performance. In order to reduce this uncertainty, this paper discusses a review of several Health Monitoring & Analysis Models in terms of their internal operational features & performance measurements. Readers will be able to recognise models that are appropriate for their application-specific use cases based on this discussion. When compared to other models, it was shown that Convolutional Neural Networks (CNNs), Masked Region CNN (MRCNN), Recurrent NN (RNN), Q-Learning, and Reinforcement learning models had greater analytical performance. They are hence suitable for clinical use cases. These models' worse scaling performance is a result of their increased complexity and higher implementation costs. This paper compares evaluated models in terms of accuracy, computational latency, deployment complexity, scalability, and deployment cost metrics to analyse such scenarios. This comparison will help users choose the best models for their performance-specific use cases. In this article, a new Health Monitoring Metric (HMM), which integrates many performance indicators to identify the best-performing models under various real-time patient settings, is reviewed to make the process of model selection even easier for real-time scenarios