Feature Representation Analysis of Deep Convolutional Neural Network using Two-stage Feature Transfer -An Application for Diffuse Lung Disease Classification-

Transfer learning is a machine learning technique designed to improve generalization performance by using pre-trained parameters obtained from other learning tasks. For image recognition tasks, many previous studies have reported that, when transfer learning is applied to deep neural networks, performance improves, despite having limited training data. This paper proposes a two-stage feature transfer learning method focusing on the recognition of textural medical images. During the proposed method, a model is successively trained with massive amounts of natural images, some textural images, and the target images. We applied this method to the classification task of textural X-ray computed tomography images of diffuse lung diseases. In our experiment, the two-stage feature transfer achieves the best performance compared to a from-scratch learning and a conventional single-stage feature transfer. We also investigated the robustness of the target dataset, based on size. Two-stage feature transfer shows better robustness than the other two learning methods. Moreover, we analyzed the feature representations obtained from DLDs imagery inputs for each feature transfer models using a visualization method. We showed that the two-stage feature transfer obtains both edge and textural features of DLDs, which does not occur in conventional single-stage feature transfer models.Comment: Preprint of the journal article to be published in IPSJ TOM-51. Notice for the use of this material The copyright of this material is retained by the Information Processing Society of Japan (IPSJ). This material is published on this web site with the agreement of the author (s) and the IPS

Note on massless bosonic states in two-dimensional field theories

In a wide class of $G_L\times G_R$ invariant two-dimensional super-renormalizable field theories, the parity-odd part of the two-point function of global currents is completely determined by a fermion one-loop diagram. For any non-trivial fermion content, the two-point function possesses a massless pole which corresponds to massless bosonic physical states. As an application, we show that two-dimensional $\mathcal{N}=(2,2)$ supersymmetric gauge theory without a superpotential possesses $U(1)_L\times U(1)_R$ symmetry and contains one massless bosonic state per fixed spatial momentum. The $\mathcal{N}=(4,4)$ supersymmetric pure Yang-Mills theory possesses $SU(2)_L\times SU(2)_R$ symmetry, and there exist at least three massless bosonic states.Comment: 17pages, 4 figures, uses PTPTeX.cls and feynMF, added an appendi

Axial U(1)U(1) symmetry at high temperature in 2-flavor lattice QCD

We investigate the axial $U(1)_A$ symmetry breaking above the critical temperature in two-flavor lattice QCD. The ensembles are generated with dynamical M\"obius domain-wall or reweighted overlap fermions. The $U(1)_A$ susceptibility is extracted from the low-modes spectrum of the overlap Dirac eigenvalues. We show the quark mass and temperature dependences of $U(1)_A$ susceptibility. Our results at $T=220 \, \mathrm{MeV}$ imply that the $U(1)_A$ symmetry is restored in the chiral limit. Its coincidence with vanishing topological susceptibility is observed.Comment: 8 pages, 4 figures, Proceedings of the 35th International Symposium on Lattice Field Theory, June 18-24, 2017, Granada, Spai

Axial U(1) symmetry and Dirac spectra in high-temperature phase of Nf=2N_f=2 lattice QCD

The axial $U(1)$ symmetry in the high-temperature phase is investigated with $N_f = 2$ lattice QCD simulations. The gauge ensembles are generated with M\"obius domain-wall fermions, and the overlap/domain-wall reweighting is applied. We find that the $U(1)_A$ susceptibility evaluated from the spectrum of overlap-Dirac eigenvalues is strongly suppressed in the chiral limit. We also study its volume dependence.Comment: 7 pages, 2 figures, talk presented at the 36th International Symposium on Lattice Field Theory (Lattice 2018), 22-28 July, 2018, Michigan, US

Delay of Onset of Symptoms of Japanese Cedar Pollinosis by Treatment with a Leukotriene Receptor Antagonist

ABSTRACTBackgroundLeukotriene receptor antagonists (LTRAs) are effective for prophylactic treatment of pollinosis based on studies showing that administration of LTRAs prior to or at the start of the pollen season reduces symptoms and QOL disturbance at the peak of pollen dispersal. Two goals of prophylactic treatment of pollinosis are use of fewer types of drugs and delay of onset of symptoms and impairement of QOL. Therefore, this study was performed to determine if pranlukast, a LTRA, met these goals in treatment of pollinosis.MethodsPranlukast or placebo was administered to patients who visited our hospital immediately before the start of Japanese cedar pollen dispersal. The study was performed for 4 weeks as a double blind randomized trial. Subsequently, all patients were given pranlukast for a further 4 weeks from the peak until the end of pollen dispersal. The incidence of symptoms and use of concomitant drugs were investigated from daily nasal allergy records kept by patients. QOL was evaluated using the JRQLQ questionnaire.ResultsIn the double blind period of the study, the percentage of patients who used concomitant drugs for nasal symptoms was significantly lower in the pranlukast group compared to the placebo group. Development of nasal symptoms (sneezing, runny nose and nasal congestion) and disturbance of daily activities were significantly delayed in the pranlukast group. No serious adverse reactions occurred in the pranlukast group and no patient withdrew from treatment with pranlukast.ConclusionsPranlukast is effective for prophylactic treatment of pollinosis