Multimodal magnetic resonance imaging (MRI) is instrumental in differentiating liver lesions. The major challenge involves modeling reliable connections and simultaneously learning complementary information across various MRI sequences. While previous studies have primarily focused on multimodal integration in a pair-wise manner using few modalities, our research seeks to advance a more comprehensive understanding of interaction modeling by establishing complex high-order correlations among the diverse modalities in multimodal MRI. In this paper, we introduce a multimodal graph learning with multi-hypergraph reasoning network to capture the full spectrum of both pair-wise and group-wise relationships among different modalities. Specifically, a weight-shared encoder extracts features from regions of interest (ROI) images across all modalities. Subsequently, a collection of uniform hypergraphs are constructed with varying vertex configurations, allowing for the modeling of not only pair-wise correlations but also the high-order collaborations for relational reasoning. Following information propagation through the hypergraph message passing, adaptive intra-modality fusion module is proposed to effectively fuse feature representations from different hypergraphs of the same modality. Finally, all refined features are concatenated to prepare for the classification task. Our experimental evaluations, including focal liver lesions classification using the LLD-MMRI2023 dataset and early recurrence prediction of hepatocellular carcinoma using our internal datasets, demonstrate that our method significantly surpasses the performance of existing approaches, indicating the effectiveness of our model in handling both pair-wise and group-wise interactions across multiple modalities.

Features are commonly used to describe the functional and non-functional characteristics of software. Especially agile development methods, such as SCRUM, FDD, or XP, use features to plan and manage software development. Features are often the main units of software reuse, communication, and configuration, abstracting over code details. Especially in the age of generative AI, where feature requirements are specified as prompts and substantial code is cloned, codebases are becoming increasingly complex and redundant. This requires raising the level of abstraction at which we manage and evolve software systems. However, effectively using features requires knowing their precise locations within codebases, which is especially challenging when they are scattered across the codebase. Once implemented, the knowledge about a feature’s location quickly deteriorates when the software evolves or development teams change, requiring expensive recovery of features. This decades-old problem is known as the feature-location or concept assignment problem in software engineering, which researchers have— unsuccessfully over decades—tried to address with automated feature-location recovery techniques. The problem lies in the common belief that recording and maintaining feature locations during development is laborious and error-prone. In this study, we argue to the contrary. We hypothesize that such information can be effectively embedded into codebases, and that the arising costs will be amortized by the benefits of this information. We validated this hypothesis in a simulation study with three subjects systems: a smaller open source system, a large commercial firmware system, and an open source mobile app. We designed a lightweight code annotation technique and simulated its use as if annotations had been added, maintained, and exploited during the original development. We identified evolution patterns and measured the cost and benefit of these annotations. Our results show that not only the cost of adding annotations, but also that of maintaining them is negligible compared to the development and maintenance costs of the actual code. Embedding the annotations into the codebase significantly reduced their maintenance effort, because they naturally co-evolved with the code. The annotations provided a benefit for feature-related maintenance tasks, such as feature cloning or merging the clones into an integrated codebase, that exceeded the costs of using them.

Liver segmentation plays a crucial role in the diagnosis and surgical planning of hepatocellular carcinoma. However, manual delineation of liver contours by radiologists is time-consuming, error-prone, and highly dependent on individual expertise. To address these challenges, we propose a Lifting Wavelet Transform-based Network (LWT-Net) for liver segmentation in CT scans. Built upon an encoder-decoder architecture, the proposed model incorporates a lifting wavelet transform module to perform multi-scale frequency-domain features. Specifically, the low-frequency components capture the global structural information, facilitating the modeling of contextual semantics, while the high-frequency components preserve finegrained details, thereby enhancing segmentation accuracy. In addition, to effectively integrate the multi-scale wavelet features with global contextual representations extracted by a CNN-based encoder, the double attention modules are applied to capture longrange spatial dependencies. Extensive experiments on two public datasets, LiTS2017 and FLARE22, demonstrate that LWT-Net achieves superior performance, with average Dice coefficients of 95.75% and 95.97%, respectively, significantly outperforming existing state-of-the-art methods. The source code will be publicly available on GitHub.

Medical image segmentation is crucial for diagnosis and surgical planning of liver diseases. The existing methods mainly focus on global or local features and neglect spatial dependencies among modalities and blurred boundaries. To tackle these challenges, we propose a symmetric bi-branch modality-search aggregation network (SBMANet). Specifically, we first design a symmetric network with dual encoder-decoder structure. Each encoder fuses two adjacent modal features to improve the intra-modal spatial information while the decoder can achieve accurate localization of segmented targets. To fully exploit multi-modal inter-modal dependencies, a hybrid Hadamard multimodal fusion module (HHMF) is proposed. Finally, we establish an adaptive modality-channel-search module by incorporating bi-branch features to automatically compute weights for each channel in different modalities. Extensive experiments on DLDS demonstrate that the proposed network outperforms existing state-of-the-art 3D segmentation networks. The code is available at the website: https://github.com/fangchj2002/SBMANet.

Features are commonly used to describe functional and nonfunctional aspects of software. To effectively evolve and reuse features, their location in software assets has to be known. However, locating features is often difficult given their crosscutting nature. Once implemented, the knowledge about a feature's location quickly deteriorates, requiring expensive recovering of these locations. Manually recording and maintaining traceability information is generally considered expensive and error-prone. In this paper, we argue to the contrary and hypothesize that such information can be effectively embedded into software assets, and that arising costs will be amortized by the benefits of this information later during development. We test this hypothesis in a study where we simulate the development of a product line of cloned/forked projects using a lightweight code annotation approach. We identify annotation evolution patterns and measure the cost and benefit of these annotations. Our results show that not only the cost of adding annotations, but also that of maintaining them is small compared to the actual development cost. Embedding the annotations into assets significantly reduced the maintenance cost because they naturally co-evolve with the assets. Our results also show that a majority of these annotations provides a benefit for feature-related code maintenance tasks, such as feature propagation and migrating clones into a platform.