Bridging Accuracy and Interpretability: Deep Learning with XAI for Breast Cancer Detection

The article discusses the limitations of saliency maps used in deep learning for visual explanations. It highlights the lack of consensus on their purpose and alignment with user queries, which affects their evaluation and utility. To address this, the authors propose the Reference-Frame × Granularity (RFxG) taxonomy, which categorizes saliency explanations based on two axes: Reference-Frame, distinguishing between pointwise and contrastive explanations, and Granularity, ranging from fine-grained to coarse-grained interpretations. This framework aims to improve the evaluation metrics for salie…

A recent study has developed a graph neural network (GNN)-based model for real-time prediction of breast cancer sites during biopsy procedures. This model addresses the challenges of creating accurate deformable breast models, which are essential for indirect MRI-guided biopsies. By incorporating individual-specific finite element models derived from MRI images, the approach aims to enhance the accuracy and efficiency of breast cancer diagnosis, ultimately improving patient outcomes.

The article presents RETROFIT, a novel continual learning method designed for security applications. Traditional deep learning models often struggle to adapt to evolving threat landscapes, leading to performance degradation. RETROFIT addresses this by enabling effective knowledge transfer without the need for historical data, thus mitigating the challenges of forgetting while integrating new information.

The article presents a new method for computing tomographic projections of a 3D volume using a linear combination of shifted B-splines. This method employs a ray-tracing algorithm to calculate 3D line integrals with various projection geometries. A neural network is integrated into the algorithm to efficiently compute the contributions of the basis functions, resulting in higher reconstruction quality compared to traditional voxel-based methods.

The study addresses the challenge of blood coagulation after drawing, which can lead to inaccurate hematological analyses. It presents a deep learning-enhanced framework utilizing Digital Holographic Microscopy (DHM) to evaluate anticoagulant efficacy ex vivo. The method allows for label-free, non-invasive analysis of human blood samples, focusing on cell counting and morphology. The research compares the effects of conventional EDTA and potassium ferric oxalate nanoparticles (KFeOx-NPs) on blood coagulation rates, revealing significant findings.

SimuFreeMark is a proposed watermarking framework designed to enhance image security against editing attacks, particularly in the context of artificial intelligence-generated content (AIGC). Unlike existing methods that depend on noise simulation, SimuFreeMark directly embeds watermarks into the low-frequency components of images, which have shown significant robustness against various attacks. This innovation aims to address the growing need for reliable watermarking solutions in an era of advanced image manipulation techniques.

An AI-assisted Augmented Reality (AR) assembly workflow has been developed, utilizing deep learning-based object recognition to identify assembly components and provide step-by-step instructions. The system displays bounding boxes around components in real-time, indicating their placement, thus eliminating the need for manual searching or sorting. A case study involving the assembly of LEGO sculptures demonstrates the system's feasibility and effectiveness in enhancing the assembly process.

NervePool is a newly proposed pooling layer designed for deep learning applications involving graph-structured data. It focuses on simplicial complexes, which extend traditional graphs by incorporating higher-dimensional simplices. This innovation aims to enhance the modeling of complex relationships while addressing issues such as computational efficiency and overfitting. The pooling layer operates through a learned hierarchical representation, allowing for effective down sampling and information compression.