3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes

While deep convolutional neural networks (CNN) have been successfully applied for 2D image analysis, it is still challenging to apply them to 3D anisotropic volumes, especially when the within-slice resolution is much higher than the between-slice resolution and when the amount of 3D volumes is relatively small. On one hand, direct learning of CNN with 3D convolution kernels suffers from the lack of data and likely ends up with poor generalization; insufficient GPU memory limits the model size or representational power. On the other hand, applying 2D CNN with generalizable features to 2D slices ignores between-slice information. Coupling 2D network with LSTM to further handle the between-slice information is not optimal due to the difficulty in LSTM learning. To overcome the above challenges, we propose a 3D Anisotropic Hybrid Network (AH-Net) that transfers convolutional features learned from 2D images to 3D anisotropic volumes. Such a transfer inherits the desired strong generalization capability for within-slice information while naturally exploiting between-slice information for more effective modelling. The focal loss is further utilized for more effective end-to-end learning. We experiment with the proposed 3D AH-Net on two different medical image analysis tasks, namely lesion detection from a Digital Breast Tomosynthesis volume, and liver and liver tumor segmentation from a Computed Tomography volume and obtain the state-of-the-art results

Alpha scattering and capture reactions in the A = 7 system at low energies

Differential cross sections for $^3$He-$\alpha$ scattering were measured in the energy range up to 3 MeV. These data together with other available experimental results for $^3$He $+ \alpha$ and $^3$H $+ \alpha$ scattering were analyzed in the framework of the optical model using double-folded potentials. The optical potentials obtained were used to calculate the astrophysical S-factors of the capture reactions $^3$He$(\alpha,\gamma)^7$Be and $^3$H$(\alpha,\gamma)^7$Li, and the branching ratios for the transitions into the two final $^7$Be and $^7$Li bound states, respectively. For $^3$He$(\alpha,\gamma)^7$Be excellent agreement between calculated and experimental data is obtained. For $^3$H$(\alpha,\gamma)^7$Li a $S(0)$ value has been found which is a factor of about 1.5 larger than the adopted value. For both capture reactions a similar branching ratio of $R = \sigma(\gamma_1)/\sigma(\gamma_0) \approx 0.43$ has been obtained.Comment: submitted to Phys.Rev.C, 34 pages, figures available from one of the authors, LaTeX with RevTeX, IK-TUW-Preprint 930540

Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists.

BACKGROUND: Artificial intelligence (AI) systems performing at radiologist-like levels in the evaluation of digital mammography (DM) would improve breast cancer screening accuracy and efficiency. We aimed to compare the stand-alone performance of an AI system to that of radiologists in detecting breast cancer in DM. METHODS: Nine multi-reader, multi-case study datasets previously used for different research purposes in seven countries were collected. Each dataset consisted of DM exams acquired with systems from four different vendors, multiple radiologists' assessments per exam, and ground truth verified by histopathological analysis or follow-up, yielding a total of 2652 exams (653 malignant) and interpretations by 101 radiologists (28 296 independent interpretations). An AI system analyzed these exams yielding a level of suspicion of cancer present between 1 and 10. The detection performance between the radiologists and the AI system was compared using a noninferiority null hypothesis at a margin of 0.05. RESULTS: The performance of the AI system was statistically noninferior to that of the average of the 101 radiologists. The AI system had a 0.840 (95% confidence interval [CI] = 0.820 to 0.860) area under the ROC curve and the average of the radiologists was 0.814 (95% CI = 0.787 to 0.841) (difference 95% CI = -0.003 to 0.055). The AI system had an AUC higher than 61.4% of the radiologists. CONCLUSIONS: The evaluated AI system achieved a cancer detection accuracy comparable to an average breast radiologist in this retrospective setting. Although promising, the performance and impact of such a system in a screening setting needs further investigation

Modern theories of low-energy astrophysical reactions

We summarize recent ab initio studies of low-energy electroweak reactions of astrophysical interest, relevant for both big bang nucleosynthesis and solar neutrino production. The calculational methods include direct integration for np radiative and pp weak capture, correlated hyperspherical harmonics for reactions of A=3,4 nuclei, and variational Monte Carlo for A=6,7 nuclei. Realistic nucleon-nucleon and three-nucleon interactions and consistent current operators are used as input.Comment: 29 pages, 4 figure

Off-shell effects in the energy dependence of the Be7(p,gamma)B8 astrophysical S factor

I show that off-shell effects, like antisymmetrization and Be-7 distortions, can significantly influence the energy dependence of the nonresonant Be7(p,gamma)B8 astrophysical S factor at higher energies. The proper treatment of these effects results in a vitrually flat E1 component of the S factor at E_cm = 0.3-1.5 MeV energies in the present eight-body model. The energy dependence of the nonresonant S factor, predicted by the present model, is in agreement with the low-energy direct capture data and the existing high-energy Coulomb dissociation data. Irrespective of whether or not the present energy dependence is correct, off-shell effects can cause 15--20% changes in the value of S(0) extrapolated from high-energy (E_cm > 0.7 MeV) data.Comment: 9 pages, 1 figur

A microscopic three-cluster model with nuclear polarization applied to the resonances of 7Be and the reaction 6Li(p,3He)4He

A microscopic model for three-cluster configurations in light nuclei is presented. It uses an expansion in terms of Faddeev components for which the dynamic eqations are derived. The model is designed to investigate binary channel processes in a compound system. Gaussian and oscillator bases are used to expand the wave function and to represent appropriate boundary conditions. We study the effect of cluster polarization on ground and resonance states of 7Be, and on the astrophysical S-factor of the reaction 6Li(p,3He)4He.Comment: 20 pages, 8 Postscript figures, uses elsart1p.sty, submitted to Nucl. Phys.