11,052 research outputs found
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
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
Cross-Modal Data Programming Enables Rapid Medical Machine Learning
Labeling training datasets has become a key barrier to building medical
machine learning models. One strategy is to generate training labels
programmatically, for example by applying natural language processing pipelines
to text reports associated with imaging studies. We propose cross-modal data
programming, which generalizes this intuitive strategy in a
theoretically-grounded way that enables simpler, clinician-driven input,
reduces required labeling time, and improves with additional unlabeled data. In
this approach, clinicians generate training labels for models defined over a
target modality (e.g. images or time series) by writing rules over an auxiliary
modality (e.g. text reports). The resulting technical challenge consists of
estimating the accuracies and correlations of these rules; we extend a recent
unsupervised generative modeling technique to handle this cross-modal setting
in a provably consistent way. Across four applications in radiography, computed
tomography, and electroencephalography, and using only several hours of
clinician time, our approach matches or exceeds the efficacy of
physician-months of hand-labeling with statistical significance, demonstrating
a fundamentally faster and more flexible way of building machine learning
models in medicine
Overview of the ImageCLEF 2016 Medical Task
ImageCLEF is the image retrieval task of the Conference and Labs of the Evaluation Forum (CLEF). ImageCLEF has historically focused on the multimodal and language–independent retrieval of images. Many tasks are related to image classification and the annotation of image data as well. The medical task has focused more on image retrieval in the beginning and then retrieval and classification tasks in subsequent years. In 2016 a main focus was the creation of meta data for a collection of medical images taken from articles of the the biomedical scientific literature. In total 8 teams participated in the four tasks and 69 runs were submitted. No team participated in the caption prediction task, a totally new task.
Deep learning has now been used for several of the ImageCLEF tasks and by many of the participants obtaining very good results. A majority of runs was submitting using deep learning and this follows general trends in machine learning. In several of the tasks multimodal approaches clearly led to best results
Deep Multi-Modal Classification of Intraductal Papillary Mucinous Neoplasms (IPMN) with Canonical Correlation Analysis
Pancreatic cancer has the poorest prognosis among all cancer types.
Intraductal Papillary Mucinous Neoplasms (IPMNs) are radiographically
identifiable precursors to pancreatic cancer; hence, early detection and
precise risk assessment of IPMN are vital. In this work, we propose a
Convolutional Neural Network (CNN) based computer aided diagnosis (CAD) system
to perform IPMN diagnosis and risk assessment by utilizing multi-modal MRI. In
our proposed approach, we use minimum and maximum intensity projections to ease
the annotation variations among different slices and type of MRIs. Then, we
present a CNN to obtain deep feature representation corresponding to each MRI
modality (T1-weighted and T2-weighted). At the final step, we employ canonical
correlation analysis (CCA) to perform a fusion operation at the feature level,
leading to discriminative canonical correlation features. Extracted features
are used for classification. Our results indicate significant improvements over
other potential approaches to solve this important problem. The proposed
approach doesn't require explicit sample balancing in cases of imbalance
between positive and negative examples. To the best of our knowledge, our study
is the first to automatically diagnose IPMN using multi-modal MRI.Comment: Accepted for publication in IEEE International Symposium on
Biomedical Imaging (ISBI) 201
- …