FTSNet: Fundus Tumor Segmentation Network on Multiple Scales Guided by Classification Results and Prompts.

The segmentation of fundus tumors is critical for ophthalmic diagnosis and treatment, yet it presents unique challenges due to the variability in lesion size and shape. Our study introduces Fundus Tumor Segmentation Network (FTSNet), a novel segmentation network designed to address these challenges by leveraging classification results and prompt learning. Our key innovation is the multiscale feature extractor and the dynamic prompt head. Multiscale feature extractors are proficient in eliciting a spectrum of feature information from the original image across disparate scales. This proficiency is fundamental for deciphering the subtle details and patterns embedded in the image at multiple levels of granularity. Meanwhile, a dynamic prompt head is engineered to engender bespoke segmentation heads for each image, customizing the segmentation process to align with the distinctive attributes of the image under consideration. We also present the Fundus Tumor Segmentation (FTS) dataset, comprising 254 pairs of fundus images with tumor lesions and reference segmentations. Experiments demonstrate FTSNet's superior performance over existing methods, achieving a mean Intersection over Union (mIoU) of 0.8254 and mean Dice (mDice) of 0.9042. The results highlight the potential of our approach in advancing the accuracy and efficiency of fundus tumor segmentation.

FNeXter: A Multi-Scale Feature Fusion Network Based on ConvNeXt and Transformer for Retinal OCT Fluid Segmentation.

The accurate segmentation and quantification of retinal fluid in Optical Coherence Tomography (OCT) images are crucial for the diagnosis and treatment of ophthalmic diseases such as age-related macular degeneration. However, the accurate segmentation of retinal fluid is challenging due to significant variations in the size, position, and shape of fluid, as well as their complex, curved boundaries. To address these challenges, we propose a novel multi-scale feature fusion attention network (FNeXter), based on ConvNeXt and Transformer, for OCT fluid segmentation. In FNeXter, we introduce a novel global multi-scale hybrid encoder module that integrates ConvNeXt, Transformer, and region-aware spatial attention. This module can capture long-range dependencies and non-local similarities while also focusing on local features. Moreover, this module possesses the spatial region-aware capabilities, enabling it to adaptively focus on the lesions regions. Additionally, we propose a novel self-adaptive multi-scale feature fusion attention module to enhance the skip connections between the encoder and the decoder. The inclusion of this module elevates the model's capacity to learn global features and multi-scale contextual information effectively. Finally, we conduct comprehensive experiments to evaluate the performance of the proposed FNeXter. Experimental results demonstrate that our proposed approach outperforms other state-of-the-art methods in the task of fluid segmentation.

Incorporating fantasy into gamification promotes student learning and quality of online interaction.

We used the design-based research approach to test and refine a theoretically grounded goal-access-feedback-challenge-collaboration gamification model. The testbed was a 10-week, university-level e-learning design course offered in two consecutive semesters. In Study 1, we implemented the initial goal-access-feedback-challenge-collaboration model in semester one of the 2020-2021 academic year (N = 26). The aim was to enhance student behavioral engagement in online discussion forums, affective engagement in the class, and learning performance. The results of Study 1 showed that although most participants were engaged in this gamified learning experience during the first two sessions, they gradually lost interest and their participation in online discussions dropped over the next eight weeks. Thus, we introduced a new element, fantasy, into the original model. In Study 2, we tested the effectiveness of the goal-access-feedback-challenge-collaboration-fantasy model on students' learning outcomes in semester two of 2020-2021 (N = 23). The results of Study 2 suggested that, compared to the original model, the goal-access-feedback-challenge-collaboration-fantasy model can better promote students' engagement in online discussion, as measured by increased interaction with peers, learning experience, and learning performance.

Transitioning to the "new normal" of learning in unpredictable times: pedagogical practices and learning performance in fully online flipped classrooms.

The COVID-19 outbreak has compelled many universities to immediately switch to the online delivery of lessons. Many instructors, however, have found developing effective online lessons in a very short period of time very stressful and difficult. This study describes how we successfully addressed this crisis by transforming two conventional flipped classes into fully online flipped classes with the help of a cloud-based video conferencing app. As in a conventional flipped course, in a fully online flipped course students are encouraged to complete online pre-class work. But unlike in the conventional flipped approach, students do not subsequently meet face-to-face in physical classrooms, but rather online. This study examines the effect of fully online flipped classrooms on student learning performance in two stages. In Stage One, we explain how we drew on the 5E framework to design two conventional flipped classes. The 5E framework consists of five phases-Engage, Explore, Explain, Elaborate, and Evaluate. In Stage Two, we describe how we transformed the two conventional flipped classes into fully online flipped classes. Quantitative analyses of students' final course marks reveal that the participants in the fully online flipped classes performed as effectively as participants in the conventional flipped learning classes. Our qualitative analyses of student and staff reflection data identify seven good practices for videoconferencing-assisted online flipped classrooms.