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

Using Crowdsourcing for Multi-label Biomedical Compound Figure Annotation

Information analysis or retrieval for images in the biomedical literature needs to deal with a large amount of compound figures (figures containing several subfigures), as they constitute probably more than half of all images in repositories such as PubMed Central, which was the data set used for the task. The ImageCLEFmed benchmark proposed among other tasks in 2015 and 2016 a multi-label classification task, which aims at evaluating the automatic classification of figures into 30 image types. This task was based on compound figures and thus the figures were distributed to participants as compound figures but also in a separated form. Therefore, the generation of a gold standard was required, so that algorithms of participants can be evaluated and compared. This work presents the process carried out to generate the multi-labels of ∼2650 compound figures using a crowdsourcing approach. Automatic algorithms to separate compound figures into subfigures were used and the results were then validated or corrected via crowdsourcing. The image types (MR, CT, X–ray, ...) were also annotated by crowdsourcing including detailed quality control. Quality control is necessary to insure quality of the annotated data as much as possible. ∼625 h were invested with a cost of ∼870$

Overview of the ImageCLEF 2015 medical classification task

This articles describes the ImageCLEF 2015 Medical Clas-sification task. The task contains several subtasks that all use a dataset of figures from the biomedical open access literature (PubMed Cen-tral). Particularly compound figures are targeted that are frequent inthe literature. For more detailed information analysis and retrieval it isimportant to extract targeted information from the compound figures.The proposed tasks include compound figure detection (separating com-pound from other figures), multi–label classification (define all sub typespresent), figure separation (find boundaries of the subfigures) and modal-ity classification (detecting the figure type of each subfigure). The tasksare described with the participation of international research groups inthe tasks. The results of the participants are then described and analysedto identify promising techniques

Ontology-based annotation using naive Bayes and decision trees

The Cognitive Paradigm Ontology (CogPO) defines an ontological relationship between academic terms and experiments in the field of neuroscience. BrainMap (www.brainmap.org) is a database of literature describing these experiments, which are annotated by human experts based on the ontological framework defined in CogPO. We present a stochastic approach to automate this process. We begin with a gold standard corpus of abstracts annotated by experts, and model the annotations with a group of naive Bayes classifiers, then explore the inherent relationship among different components defined by the ontology using a probabilistic decision tree model. Our solution outperforms conventional text mining approaches by taking advantage of an ontology. We consider five essential ontological components (Stimulus Modality, Stimulus Type, Response Modality, Response Type, and Instructions) in CogPO, evaluate the probability of successfully categorizing a research paper on each component by training a basic multi-label naive Bayes classifier with a set of examples taken from the BrainMap database which are already manually annotated by human experts. According to the performance of the classifiers we create a decision tree to label the components sequentially on different levels. Each node of the decision tree is associated with a naive Bayes classifier built in different subspaces of the input universe. We first make decisions on those components whose labels are comparatively easy to predict, and then use these predetermined conditions to narrow down the input space along all tree paths, therefore boosting the performance of the naive Bayes classification upon components whose labels are difficult to predict. For annotating a new instance, we use the classifiers associated with the nodes to find labels for each component, starting from the root and then tracking down the tree perhaps on multiple paths. The annotation is completed when the bottom level is reached, where all labels produced along the paths are collected

Eye-CU: Sleep Pose Classification for Healthcare using Multimodal Multiview Data

Manual analysis of body poses of bed-ridden patients requires staff to continuously track and record patient poses. Two limitations in the dissemination of pose-related therapies are scarce human resources and unreliable automated systems. This work addresses these issues by introducing a new method and a new system for robust automated classification of sleep poses in an Intensive Care Unit (ICU) environment. The new method, coupled-constrained Least-Squares (cc-LS), uses multimodal and multiview (MM) data and finds the set of modality trust values that minimizes the difference between expected and estimated labels. The new system, Eye-CU, is an affordable multi-sensor modular system for unobtrusive data collection and analysis in healthcare. Experimental results indicate that the performance of cc-LS matches the performance of existing methods in ideal scenarios. This method outperforms the latest techniques in challenging scenarios by 13% for those with poor illumination and by 70% for those with both poor illumination and occlusions. Results also show that a reduced Eye-CU configuration can classify poses without pressure information with only a slight drop in its performance.Comment: Ten-page manuscript including references and ten figure