Pathologists face a substantial increase in workload and complexity of histopathologic cancer diagnosis due to the advent of personalized medicine. Therefore, diagnostic protocols have to focus ...equally on efficiency and accuracy. In this paper we introduce 'deep learning' as a technique to improve the objectivity and efficiency of histopathologic slide analysis. Through two examples, prostate cancer identification in biopsy specimens and breast cancer metastasis detection in sentinel lymph nodes, we show the potential of this new methodology to reduce the workload for pathologists, while at the same time increasing objectivity of diagnoses. We found that all slides containing prostate cancer and micro- and macro-metastases of breast cancer could be identified automatically while 30-40% of the slides containing benign and normal tissue could be excluded without the use of any additional immunohistochemical markers or human intervention. We conclude that 'deep learning' holds great promise to improve the efficacy of prostate cancer diagnosis and breast cancer staging.
From the intestinal flora to the microbiome Sebastián Domingo, Juan José; Sánchez Sánchez, Clara
Revista española de enfermedades digestivas,
01/2018, Letnik:
110, Številka:
1
Journal Article
Recenzirano
Odprti dostop
In this article, the history of the microbiota is reviewed and the related concepts of the microbiota, microbiome, metagenome, pathobiont, dysbiosis, holobiont, phylotype and enterotype are defined. ...The most precise and current knowledge about the microbiota is presented and the metabolic, nutritional and immunomodulatory functions are reviewed. Some gastrointestinal diseases whose pathogenesis is associated with the intestinal microbiota, including inflammatory bowel disease, irritable bowel syndrome and celiac disease, among others, are briefly discussed. Finally, some prominent and promising data with regard to the fecal microbiota transplantation in certain digestive illness are discussed.
A survey on deep learning in medical image analysis Litjens, Geert; Kooi, Thijs; Bejnordi, Babak Ehteshami ...
Medical image analysis,
December 2017, 2017-Dec, 2017-12-00, 20171201, Letnik:
42
Journal Article
Recenzirano
Odprti dostop
•A summary of all deep learning algorithms used in medical image analysis is given.•The most successful algorithms for key image analysis tasks are identified.•300 papers applying deep learning to ...different applications have been summarized.
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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. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal. We end with a summary of the current state-of-the-art, a critical discussion of open challenges and directions for future research.
•A system based on deep learning is shown to outperform a state-of-the art CAD system.•Adding complementary handcrafted features to the CNN is shown to increase performance.•The system based on deep ...learning is shown to perform at the level of a radiologist.
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Recent advances in machine learning yielded new techniques to train deep neural networks, which resulted in highly successful applications in many pattern recognition tasks such as object detection and speech recognition. In this paper we provide a head-to-head comparison between a state-of-the art in mammography CAD system, relying on a manually designed feature set and a Convolutional Neural Network (CNN), aiming for a system that can ultimately read mammograms independently. Both systems are trained on a large data set of around 45,000 images and results show the CNN outperforms the traditional CAD system at low sensitivity and performs comparable at high sensitivity. We subsequently investigate to what extent features such as location and patient information and commonly used manual features can still complement the network and see improvements at high specificity over the CNN especially with location and context features, which contain information not available to the CNN. Additionally, a reader study was performed, where the network was compared to certified screening radiologists on a patch level and we found no significant difference between the network and the readers.
Glaucoma detection in color fundus images is a challenging task that requires expertise and years of practice. In this study we exploited the application of different Convolutional Neural Networks ...(CNN) schemes to show the influence in the performance of relevant factors like the data set size, the architecture and the use of transfer learning vs newly defined architectures. We also compared the performance of the CNN based system with respect to human evaluators and explored the influence of the integration of images and data collected from the clinical history of the patients. We accomplished the best performance using a transfer learning scheme with VGG19 achieving an AUC of 0.94 with sensitivity and specificity ratios similar to the expert evaluators of the study. The experimental results using three different data sets with 2313 images indicate that this solution can be a valuable option for the design of a computer aid system for the detection of glaucoma in large-scale screening programs.
Zooplankton plays a pivotal role in sustaining the majority of marine ecosystems. The distribution patterns and diversity of zooplankton provide key information for understanding the functioning of ...these ecosystems. Nevertheless, due to the numerous cryptic and sibling species and the lack of diagnostic characteristics for early developmental stages, the identification of the global‐to‐local patterns of zooplankton biodiversity and biogeography remains challenging in different research fields. The spatial and temporal changes in the zooplankton community in the open waters of the southern Gulf of Mexico were assessed using metabarcoding analysis of the V9 region of 18S rRNA and mitochondrial cytochrome oxidase c subunit I (COI). Additionally, a multiscale analysis was implemented to evaluate which environmental predictors may explain the variability in the structure of the zooplankton community. Our findings suggest that the synergistic effects of dissolved oxygen concentration, temperature, and longitude (intended as a proxy for still unidentified predictors) may explain both spatial and temporal zooplankton variability even with low contribution. Furthermore, the zooplankton distribution probably reflects the coexistence of three heterogeneous ecoregions and a bio‐physical partitioning of the studied area. Finally, some taxa were either exclusive or predominant with either 18S or COI markers. This may suggest that comprehensive assessments of the zooplankton community may be more accurately met by the use of multilocus approaches.
To evaluate a machine learning algorithm that automatically grades age-related macular degeneration (AMD) severity stages from optical coherence tomography (OCT) scans.
A total of 3265 OCT scans from ...1016 patients with either no signs of AMD or with signs of early, intermediate, or advanced AMD were randomly selected from a large European multicenter database. A machine learning system was developed to automatically grade unseen OCT scans into different AMD severity stages without requiring retinal layer segmentation. The ability of the system to identify high-risk AMD stages and to assign the correct severity stage was determined by using receiver operator characteristic (ROC) analysis and Cohen's κ statistics (κ), respectively. The results were compared to those of two human observers. Reproducibility was assessed in an independent, publicly available data set of 384 OCT scans.
The system achieved an area under the ROC curve of 0.980 with a sensitivity of 98.2% at a specificity of 91.2%. This compares favorably with the performance of human observers who achieved sensitivities of 97.0% and 99.4% at specificities of 89.7% and 87.2%, respectively. A good level of agreement with the reference was obtained (κ = 0.713) and was in concordance with the human observers (κ = 0.775 and κ = 0.755, respectively).
A machine learning system capable of automatically grading OCT scans into AMD severity stages was developed and showed similar performance as human observers. The proposed automatic system allows for a quick and reliable grading of large quantities of OCT scans, which could increase the efficiency of large-scale AMD studies and pave the way for AMD screening using OCT.
Environmental adaptation of organisms relies on fast perception and response to external signals, which lead to developmental changes. Plant cell growth is strongly dependent on cell wall remodeling. ...However, little is known about cell wall‐related sensing of biotic stimuli and the downstream mechanisms that coordinate growth and defense responses. We generated genetically encoded pH sensors to determine absolute pH changes across the plasma membrane in response to biotic stress. A rapid apoplastic acidification by phosphorylation‐based proton pump activation in response to the fungus Fusarium oxysporum immediately reduced cellulose synthesis and cell growth and, furthermore, had a direct influence on the pathogenicity of the fungus. In addition, pH seems to influence cellulose structure. All these effects were dependent on the COMPANION OF CELLULOSE SYNTHASE proteins that are thus at the nexus of plant growth and defense. Hence, our discoveries show a remarkable connection between plant biomass production, immunity, and pH control, and advance our ability to investigate the plant growth‐defense balance.
Synopsis
Plant pathogens, including the fungus Fusarium oxysporum, modulate plant apoplastic pH to facilitate infection. Here, F. oxysporum infection is shown to induce a rapid plant response via pH‐dependent modulation of cell wall structure and root growth, which controls fungal pathogenesis.
F. oxysporum infection triggers reduction of cellulose synthesis and inhibition of root growth.
Genetically encoded pH sensors show rapid plant apoplastic acidification upon F. oxysporum contact due to proton pump activation at the plasma membrane.
Infection‐induced pH changes influence plant cellulose synthesis, growth, and fungal virulence.
The COMPANION OF CELLULOSE SYNTHASE proteins CC1 and CC2 suppress proton pump activity.
cc1cc2 mutant plants are less sensitive to fungal infection.
Induction of apoplast acidification and modification of cell wall properties to control fungal infection are regulated by the COMPANION OF CELLULOSE SYNTHASE proteins at the nexus of plant growth and defense.
Tuberculosis (TB) is a common disease with high mortality and morbidity rates worldwide. Automatic systems to detect TB on chest radiographs (CXRs) can improve the efficiency of diagnostic algorithms ...for pulmonary TB. The diverse manifestation of TB on CXRs from different populations requires a system that can be adapted to deal with different types of abnormalities. A computer aided detection (CAD) system was developed which combines several subscores of supervised subsystems detecting textural, shape, and focal abnormalities into one TB score. A general framework was developed to combine an arbitrary number of subscores: subscores were normalized, collected in a feature vector and then combined using a supervised classifier into one combined score. The method was evaluated on two databases, both consisting of 200 digital CXRs, from: (A) Western high-risk group screening, (B) TB suspect screening in Africa. The subscores and combined score were compared to (1) an external, non-radiological, reference and (2) a radiological reference determined by a human expert. Performance was measured using Receiver Operator Characteristic (ROC) analysis. Different subscores performed best in the two databases. The combined TB score performed better than the individual subscores, except for the external reference in database B. The performances of the independent observer were slightly higher than the combined TB score. Compared to the external reference, differences in performance between the combined TB score and the independent observer were not significant in both databases. Supervised combination to compute an overall TB score allows for a necessary adaptation of the CAD system to different settings or different operational requirements.
Purpose
To validate the performance of a commercially available, CE‐certified deep learning (DL) system, RetCAD v.1.3.0 (Thirona, Nijmegen, The Netherlands), for the joint automatic detection of ...diabetic retinopathy (DR) and age‐related macular degeneration (AMD) in colour fundus (CF) images on a dataset with mixed presence of eye diseases.
Methods
Evaluation of joint detection of referable DR and AMD was performed on a DR‐AMD dataset with 600 images acquired during routine clinical practice, containing referable and non‐referable cases of both diseases. Each image was graded for DR and AMD by an experienced ophthalmologist to establish the reference standard (RS), and by four independent observers for comparison with human performance. Validation was furtherly assessed on Messidor (1200 images) for individual identification of referable DR, and the Age‐Related Eye Disease Study (AREDS) dataset (133 821 images) for referable AMD, against the corresponding RS.
Results
Regarding joint validation on the DR‐AMD dataset, the system achieved an area under the ROC curve (AUC) of 95.1% for detection of referable DR (SE = 90.1%, SP = 90.6%). For referable AMD, the AUC was 94.9% (SE = 91.8%, SP = 87.5%). Average human performance for DR was SE = 61.5% and SP = 97.8%; for AMD, SE = 76.5% and SP = 96.1%. Regarding detection of referable DR in Messidor, AUC was 97.5% (SE = 92.0%, SP = 92.1%); for referable AMD in AREDS, AUC was 92.7% (SE = 85.8%, SP = 86.0%).
Conclusion
The validated system performs comparably to human experts at simultaneous detection of DR and AMD. This shows that DL systems can facilitate access to joint screening of eye diseases and become a quick and reliable support for ophthalmological experts.
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