Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation

Image segmentation is a fundamental problem in biomedical image analysis. Recent advances in deep learning have achieved promising results on many biomedical image segmentation benchmarks. However, due to large variations in biomedical images (different modalities, image settings, objects, noise, etc), to utilize deep learning on a new application, it usually needs a new set of training data. This can incur a great deal of annotation effort and cost, because only biomedical experts can annotate effectively, and often there are too many instances in images (e.g., cells) to annotate. In this paper, we aim to address the following question: With limited effort (e.g., time) for annotation, what instances should be annotated in order to attain the best performance? We present a deep active learning framework that combines fully convolutional network (FCN) and active learning to significantly reduce annotation effort by making judicious suggestions on the most effective annotation areas. We utilize uncertainty and similarity information provided by FCN and formulate a generalized version of the maximum set cover problem to determine the most representative and uncertain areas for annotation. Extensive experiments using the 2015 MICCAI Gland Challenge dataset and a lymph node ultrasound image segmentation dataset show that, using annotation suggestions by our method, state-of-the-art segmentation performance can be achieved by using only 50% of training data.Comment: Accepted at MICCAI 201

Higgs signals and hard photons at the Next Linear Collider: the ZZZZ-fusion channel in the Standard Model

In this paper, we extend the analyses carried out in a previous article for $WW$-fusion to the case of Higgs production via $ZZ$-fusion within the Standard Model at the Next Linear Collider, in presence of electromagnetic radiation due real photon emission. Calculations are carried out at tree-level and rates of the leading order (LO) processes e^+e^-\rightarrow e^+e^- H \ar e^+e^- b\bar b and e^+e^-\rightarrow e^+e^- H \ar e^+e^- WW \ar e^+e^- \mathrm{jjjj} are compared to those of the next-to-leading order (NLO) reactions e^+e^-\rightarrow e^+e^- H (\gamma)\ar e^+e^- b\bar b \gamma and e^+e^-\rightarrow e^+e^- H (\gamma)\ar e^+e^- WW (\gamma) \ar e^+e^- \mathrm{jjjj}\gamma, in the case of energetic and isolated photons.Comment: 12 pages, LaTeX, 5 PostScript figures embedded using epsfig and bitmapped at 100dpi, complete paper including high definition figures available at ftp://axpa.hep.phy.cam.ac.uk/stefano/cavendish_9611.ps or at http://www.hep.phy.cam.ac.uk/theory/papers

Production of Doubly Charged Higgs Bosons at Linear e-e- Colliders

Production of doubly charged Higgs bosons via the s-channel process e-e- -> H-- -> l-l- at future linear collider energies is studied by taking into account initial state radiation (ISR) and beamstrahlung (ISR + BS), final state radiation (FSR) and detector smearing effects. The discovery bounds of lepton flavour conserving and violating couplings are obtained for doubly charged Higgs bosons. It is found that future linear colliders with centre of mass energies and will be able to probe the doubly charged Higgs bosons with diagonal couplings down to 10^-4 and 10^-3, respectively.Comment: 22 pages, 12 figures, 4 table

Cosmic ray tests of a GEM-based TPC prototype operated in Ar-CF4-isobutane gas mixtures

Argon with an admixture of CF4 is expected to be a good candidate for the gas mixture to be used for a time projection chamber (TPC) in the future linear collider experiment because of its small transverse diffusion of drift electrons especially under a strong magnetic field. In order to confirm the superiority of this gas mixture over conventional TPC gases we carried out cosmic ray tests using a GEM-based TPC operated mostly in Ar-CF4-isobutane mixtures under 0 - 1 T axial magnetic fields. The measured gas properties such as gas gain and transverse diffusion constant as well as the observed spatial resolution are presented.Comment: 22 pages, 18 figures. Published in Nuclear Instruments and Methods in Physics Research A. Fig. 3 in the introduction was corrected since it had not been properly normalized. Minor corrections and no changes in the conclusio

Incorporating rich background knowledge for gene named entity classification and recognition

<p>Abstract</p> <p>Background</p> <p>Gene named entity classification and recognition are crucial preliminary steps of text mining in biomedical literature. Machine learning based methods have been used in this area with great success. In most state-of-the-art systems, elaborately designed lexical features, such as words, n-grams, and morphology patterns, have played a central part. However, this type of feature tends to cause extreme sparseness in feature space. As a result, out-of-vocabulary (OOV) terms in the training data are not modeled well due to lack of information.</p> <p>Results</p> <p>We propose a general framework for gene named entity representation, called feature coupling generalization (FCG). The basic idea is to generate higher level features using term frequency and co-occurrence information of highly indicative features in huge amount of unlabeled data. We examine its performance in a named entity classification task, which is designed to remove non-gene entries in a large dictionary derived from online resources. The results show that new features generated by FCG outperform lexical features by 5.97 F-score and 10.85 for OOV terms. Also in this framework each extension yields significant improvements and the sparse lexical features can be transformed into both a lower dimensional and more informative representation. A forward maximum match method based on the refined dictionary produces an F-score of 86.2 on BioCreative 2 GM test set. Then we combined the dictionary with a conditional random field (CRF) based gene mention tagger, achieving an F-score of 89.05, which improves the performance of the CRF-based tagger by 4.46 with little impact on the efficiency of the recognition system. A demo of the NER system is available at <url>http://202.118.75.18:8080/bioner</url>.</p

Expressão gênica diferencial envolvida com Condronecrose bacteriana com osteomielite em frangos de corte com 35 dias de idade.

Resumo: A incidência de problemas ósseos é considerada uma das principais preocupações para a indústria avícola devido à perdas econômicas significativas e ao impacto negativo no bem-estar. As vias metabólicas e os genes envolvidos nas patologias ósseas permanecem desconhecidos. A condronecrose bacteriana com osteomielite (BCO) é uma das principais doenças ligadas a problemas locomotores em frangos. Na tentativa de esclarecer os mecanismos genéticos envolvidos na manifestação da BCO, objetivou-se identificar os genes diferencialmente expressos no fêmur de frangos de corte normais e afetados por esta desordem, por meio da tecnologia de RNA-Seq. Neste estudo foram utilizados frangos de corte comerciais machos aos 35 dias de idade sendo 4 normais e 4 com BCO inicial. O sequenciamento das bibliotecas foi realizado na plataforma Illumina. Os genes diferencialmente expressos foram selecionados utilizando-se o pacote EdgeR, com base no nível de confiança estatística (FDR> 0,05) e log de foldchange ? 1,0. Um total de 11.500 genes se apresentaram expressos nesse tecido ósseo, dos quais 153 foram diferencialmente expressos entre frangos normais e afetados. Após a análise de ontologia gênica alguns genes candidatos foram prospectados. O conhecimento dos genes que controlam esse distúrbio pode apoiar estratégias de melhoramento para a produção de frangos de corte comerciais resilientes para BCO, com o objetivo de se reduzir as perdas ocasionadas por problemas nas pernas na indústria avícola.Título em inglês: Gene expression related to the Bacterial Chondronecrosis with Osteomyelitis in 35 day old Broilers

Visual analytics for collaborative human-machine confidence in human-centric active learning tasks

Active machine learning is a human-centric paradigm that leverages a small labelled dataset to build an initial weak classifier, that can then be improved over time through human-machine collaboration. As new unlabelled samples are observed, the machine can either provide a prediction, or query a human ‘oracle’ when the machine is not confident in its prediction. Of course, just as the machine may lack confidence, the same can also be true of a human ‘oracle’: humans are not all-knowing, untiring oracles. A human’s ability to provide an accurate and confident response will often vary between queries, according to the duration of the current interaction, their level of engagement with the system, and the difficulty of the labelling task. This poses an important question of how uncertainty can be expressed and accounted for in a human-machine collaboration. In short, how can we facilitate a mutually-transparent collaboration between two uncertain actors - a person and a machine - that leads to an improved outcome?In this work, we demonstrate the benefit of human-machine collaboration within the process of active learning, where limited data samples are available or where labelling costs are high. To achieve this, we developed a visual analytics tool for active learning that promotes transparency, inspection, understanding and trust, of the learning process through human-machine collaboration. Fundamental to the notion of confidence, both parties can report their level of confidence during active learning tasks using the tool, such that this can be used to inform learning. Human confidence of labels can be accounted for by the machine, the machine can query for samples based on confidence measures, and the machine can report confidence of current predictions to the human, to further the trust and transparency between the collaborative parties. In particular, we find that this can improve the robustness of the classifier when incorrect sample labels are provided, due to unconfidence or fatigue. Reported confidences can also better inform human-machine sample selection in collaborative sampling. Our experimentation compares the impact of different selection strategies for acquiring samples: machine-driven, human-driven, and collaborative selection. We demonstrate how a collaborative approach can improve trust in the model robustness, achieving high accuracy and low user correction, with only limited data sample selections