Search Results (2)

Medical image segmentation is crucial for medical image processing and the development of computer-aided diagnostics. In recent years, deep Convolutional Neural Networks (CNNs) have been widely adopted for medical image segmentation and have achieved significant success. UNet, which is based on CNNs, is the mainstream method used for medical image segmentation. However, its performance suffers owing to its inability to capture long-range dependencies. Transformers were initially designed for Natural Language Processing (NLP), and sequence-to-sequence applications have demonstrated the ability to capture long-range dependencies. However, their abilities to acquire local information are limited. Hybrid architectures of CNNs and Transformer, such as TransUNet, have been proposed to benefit from Transformer’s long-range dependencies and CNNs’ low-level details. Nevertheless, automatic medical image segmentation remains a challenging task due to factors such as blurred boundaries, the low-contrast tissue environment, and in the context of ultrasound, issues like speckle noise and attenuation. In this paper, we propose a new model that combines the strengths of both CNNs and Transformer, with network architectural improvements designed to enrich the feature representation captured by the skip connections and the decoder. To this end, we devised a new attention module called Three-Level Attention (TLA). This module is composed of an Attention Gate (AG), channel attention, and spatial normalization mechanism. The AG preserves structural information, whereas channel attention helps to model the interdependencies between channels. Spatial normalization employs the spatial coefficient of the Transformer to improve spatial attention akin to TransNorm. To further improve the skip connection and reduce the semantic gap, skip connections between the encoder and decoder were redesigned in a manner similar to that of the UNet++ dense connection. Moreover, deep supervision using a side-output channel was introduced, analogous to BASNet, which was originally used for saliency predictions. Two datasets from different modalities, a CT scan dataset and an ultrasound dataset, were used to evaluate the proposed UNet architecture. The experimental results showed that our model consistently improved the prediction performance of the UNet across different datasets. Full article

Some trans people experience gender dysphoria, which refers to psychological distress that results from an incongruence between one’s gender assigned at birth and one’s gender identity. People who are trans masculine or nonbinary assigned-female-at-birth may pursue multiple domains of gender affirmation, including surgical affirmation (e.g., masculine chest reconstruction, penile reconstruction, etc.). The present study aimed to investigate the possible factors involved in trans people’s desire to undergo gender-affirming genital surgery. Trans masculine and nonbinary participants (N = 127; mean age = 26.90) were recruited through a web-based survey and completed self-report instruments (i.e., the Internalized Transphobia subscale of the Gender Minority Stress and Resilience Measure, the Trans Positive Identity Measure, the Gender Congruence and Life Satisfaction Scale, an ad hoc scale on transnormativity, and a single-item on desire to undergo genital affirmation surgery). A path analysis showed that higher levels of internalized transphobia led to more significant genital discomfort via a dual parallel mediation of transnormativity and positive identity. Moreover, this genital discomfort fueling pattern was the most significant predictor of the desire to undergo genital surgery as the effect of internalized transphobia was fully mediated by increased genital discomfort. Findings are discussed in the light of the recent strand of research on gender dysphoria as a multifaceted construct, with social components. Full article