Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification

Mammogram classification is directly related to computer-aided diagnosis of breast cancer. Traditional methods rely on regions of interest (ROIs) which require great efforts to annotate. Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning (MIL) for labeling a set of instances/patches, we propose end-to-end trained deep multi-instance networks for mass classification based on whole mammogram without the aforementioned ROIs. We explore three different schemes to construct deep multi-instance networks for whole mammogram classification. Experimental results on the INbreast dataset demonstrate the robustness of proposed networks compared to previous work using segmentation and detection annotations.Comment: MICCAI 2017 Camera Read

Magnetic structure of superconducting Eu(Fe0.82Co0.18)2As2 as revealed by single-crystal neutron diffraction

The magnetic structure of superconducting Eu(Fe0.82Co0.18)2As2 is unambiguously determined by single-crystal neutron diffraction. A long-range ferromagnetic order of the Eu2+ moments along the c-direction is revealed below the magnetic phase transition temperature Tc = 17 K. In addition, the antiferromagnetism of the Fe2+ moments still survives and the tetragonal-to-orthorhombic structural phase transition is also observed, although the transition temperatures of the Fe-spin density wave (SDW) order and the structural phase transition are significantly suppressed to Tn = 70 K and Ts = 90 K, respectively, compared to the parent compound EuFe2As2.We present the microscopic evidences for the coexistence of the Eu-ferromagnetism (FM) and the Fe-SDW in the superconducting crystal. The superconductivity (SC) competes with the Fe-SDW in Eu(Fe0.82Co0.18)2As2.Moreover, the comparison between Eu(Fe1-xCox)2As2 and Ba(Fe1-xCox)2As2 indicates a considerable influence of the rare-earth element Eu on the magnetism of the Fe sublattice.Comment: 7 pages, 7 figures, accepted for publication in Physical Review

An Attention-Guided Deep Regression Model for Landmark Detection in Cephalograms

Cephalometric tracing method is usually used in orthodontic diagnosis and treatment planning. In this paper, we propose a deep learning based framework to automatically detect anatomical landmarks in cephalometric X-ray images. We train the deep encoder-decoder for landmark detection, and combine global landmark configuration with local high-resolution feature responses. The proposed frame-work is based on 2-stage u-net, regressing the multi-channel heatmaps for land-mark detection. In this framework, we embed attention mechanism with global stage heatmaps, guiding the local stage inferring, to regress the local heatmap patches in a high resolution. Besides, the Expansive Exploration strategy improves robustness while inferring, expanding the searching scope without increasing model complexity. We have evaluated our framework in the most widely-used public dataset of landmark detection in cephalometric X-ray images. With less computation and manually tuning, our framework achieves state-of-the-art results

Studies on antioxidant capacity of anthocyanin extract from purple sweet potato (Ipomoea batatas L.)

The radical scavenging effects by α,α-diphenyl-β-picrylhydrazyl (DPPH) and superoxide anions of anthocyanin extract from purple sweet potato were investigated. The antioxidation experiments showed that the reducing power of the anthocyanin extract reduced 0.572 at 0.5 mg/ml, while those of Lascorbic acid (L-AA) and butylated hydroxytoluene (BHT) reduced 0.460 and 0.121, respectively. They also displayed potent antioxidant effects against the DPPH radical and superoxide anions radical, showing the IC50 values of 6.94 and 3.68 μg/ml, respectively. Moreover, this anthocyanin extract also could significantly inhibit the formation of lipid peroxidation compound. Sixteen kinds of anthocyanins in purple sweet potato were detected by high-performance liquid chromatography with diode-array detection (HPLC-DAD), and most of the anthocyanins were acylated.Key words: Antioxidant activity, anthocyanins, purple sweet potato

Research on the performance of radiative cooling and solar heating coupling module to direct control indoor temperature

The energy crisis and environmental pollution pose great challenges to human development. Traditional vapor-compression cooling consumes abundant energy and leads to a series of environmental problems. Radiative cooling without energy consumption and environmental pollution holds great promise as the next generation cooling technology, applied in buildings mostly in indirect way. In this work, a temperature-regulating module was introduced for direct summer cooling and winter heating. Firstly, the summer experiments were conduct to investigate the radiative cooling performance of the module. And the results indicated that the maximum indoor temperature reached only 27.5 °C with the ambient temperature of 34 °C in low latitude areas and the air conditioning system was on for only about a quarter of the day. Subsequently, the winter experiments were performed to explore the performance of the module in cooling and heating modes. The results indicated that indoor temperature can reach 25 °C in the daytime without additional heat supply and about a quarter of the day didn't require heating in winter. Additionally, the transient model of the module and the building revealed that the electricity saving of 42.4% (963.5 kWh) can be achieved in cooling season with the module, and that was 63.7% (1449.1 kWh) when coupling with energy storage system. Lastly, further discussion about the challenges and feasible solutions for radiative cooling to directly combine with the buildings were provided to advance the application of radiative cooling. Furthermore, with an acceptable payback period of 8 years, the maximum acceptable incremental cost reached 26.2 $/m2. The work opens up a new avenue for the application mode of the daytime radiative cooling technology