Optic Cup Segmentation Using Large Pixel Patch Based CNNs

Optic cup(OC) segmentation on color fundus image is essential for the calculation of cup-to-disk ratio and fundus morphological analysis, which are very important references in the diagnosis of glaucoma. In this paper we proposed an OC segmentation method using convolutional neural networks(CNNs) to learn from big size patch belong to each pixel. The segmentation result is achieved by classification of each pixel patch and postprocessing. With large pixel patch, the network could learn more global information around each pixel and make a better judgement during classification. We tested this method on public dataset Drishti-GS and achieved average F-Score of 93.73% and average overlapping error of 12.25%, which is better than state-of-the-art algorithms. This method could be used for fundus morphological analysis, and could also be employed to other medical image segmentation works which the boundary of the target area is fuzzy

Dual-attention Focused Module for Weakly Supervised Object Localization

The research on recognizing the most discriminative regions provides referential information for weakly supervised object localization with only image-level annotations. However, the most discriminative regions usually conceal the other parts of the object, thereby impeding entire object recognition and localization. To tackle this problem, the Dual-attention Focused Module (DFM) is proposed to enhance object localization performance. Specifically, we present a dual attention module for information fusion, consisting of a position branch and a channel one. In each branch, the input feature map is deduced into an enhancement map and a mask map, thereby highlighting the most discriminative parts or hiding them. For the position mask map, we introduce a focused matrix to enhance it, which utilizes the principle that the pixels of an object are continuous. Between these two branches, the enhancement map is integrated with the mask map, aiming at partially compensating the lost information and diversifies the features. With the dual-attention module and focused matrix, the entire object region could be precisely recognized with implicit information. We demonstrate outperforming results of DFM in experiments. In particular, DFM achieves state-of-the-art performance in localization accuracy in ILSVRC 2016 and CUB-200-2011.Comment: 8 pages, 6 figures and 4 table

Factors influencing householder self-evacuation in two Australian bushfires

The thesis investigated householder self-evacuation decision-making during bushfires in the Perth and Adelaide Hills in 2014 and 2015. It explored the factors that influenced householders’ decisions to evacuate, identified factors that predict self-evacuation and established the characteristics of self-evacuators. The Protective Action Decision Model (PADM) provided a conceptual framework for the research. Its theoretical and analytical usefulness in an Australian context, was assessed. A mixed methods research strategy was used involving quantitative telephone surveys of 457 bushfire-affected participants and face-to-face interviews of 109 participants in 59 households. The study concluded that environmental and social cues and warnings and householders’ perceptions of the threat, of hazard adjustments and of other stakeholders, influenced self-evacuation decision-making. Protective action perceptions, particularly the effectiveness of evacuating or not evacuating in protecting personal safety or property, were most important in predicting self-evacuation. Receipt of official warnings and the perception of likely impact of the bushfire on property were also important predictors. Undertaking long-run hazard adjustments, although not predictive of self-evacuation, was pivotal in shaping perceptions of the effectiveness of evacuating and remaining in protecting personal safety and property and indirectly influenced evacuation decisions. Seven archetypes that characterised householders’ self-evacuation attitudes and behaviour were identified. These included Threat, and Responsibility Deniers, Dependent, and Considered Evacuators, Community Guided and Experienced Independents all who took different decisional ‘rules of thumb’ and routes toward evacuating or remaining . The PADM needs to be split into two separate models to incorporate the influence of long-run hazard adjustments on protective action decision-making in an Australian bushfire. The findings suggest that future research on those who wait and see during a bushfire should take account of their decisional rules of thumb and that design and targeting of Australian bushfire safety policy should better account for self-evacuator characteristics

Re-LSTM: A long short-term memory network text similarity algorithm based on weighted word embedding

Natural language processing text similarity calculation is a crucial and difficult problem that enables matching between various messages. This approach is the foundation of many applications. The word representation features and contextual relationships extracted by current text similarity computation methods are insufficient, and too many factors increase the computational complexity. Re-LSTM, a weighted word embedding long and short-term memory network, has therefore been proposed as a text similarity computing model. The two-gate mechanism of Re-LSTM neurons is built on the foundation of the conventional LSTM model and is intended to minimise the parameters and computation to some level. The hidden features and state information of the layer above each gate are considered for extracting more implicit features. By fully utilising the feature word and its domain association, the feature word’s position, and the word frequency information, the TF-IDF method and the χ²-C algorithm may effectively improve the representation of the weights on the words. The Attention mechanism is used in Re-LSTM to combine dependencies and feature word weights for deeper text semantic mining. The experimental results demonstrate that the Re-LSTM model outperforms baselines in terms of precision, recall, accuracy, and F1 values, all of which reach above 85% when applied to the QQPC and ATEC datasets

Weakly-Supervised Simultaneous Evidence Identification and Segmentation for Automated Glaucoma Diagnosis

Evidence identification, optic disc segmentation and automated glaucoma diagnosis are the most clinically significant tasks for clinicians to assess fundus images. However, delivering the three tasks simultaneously is extremely challenging due to the high variability of fundus structure and lack of datasets with complete annotations. In this paper, we propose an innovative Weakly-Supervised Multi-Task Learning method (WSMTL) for accurate evidence identification, optic disc segmentation and automated glaucoma diagnosis. The WSMTL method only uses weak-label data with binary diagnostic labels (normal/glaucoma) for training, while obtains pixel-level segmentation mask and diagnosis for testing. The WSMTL is constituted by a skip and densely connected CNN to capture multi-scale discriminative representation of fundus structure; a well-designed pyramid integration structure to generate high-resolution evidence map for evidence identification, in which the pixels with higher value represent higher confidence to highlight the abnormalities; a constrained clustering branch for optic disc segmentation; and a fully-connected discriminator for automated glaucoma diagnosis. Experimental results show that our proposed WSMTL effectively and simultaneously delivers evidence identification, optic disc segmentation (89.6% TP Dice), and accurate glaucoma diagnosis (92.4% AUC). This endows our WSMTL a great potential for the effective clinical assessment of glaucoma