Phosphatidylinositol-4, 5-Bisphosphate 3-Kinase Catalytic Subunit Alpha (PIK3CA) gene mutations are among the most common somatic mutations in cancer, particularly in hormone receptor-positive breast cancer, with a mutation rate as high as 40 %. They are crucial for guiding targeted therapies in precision medicine. However, traditional detection methods, such as tissue-based next-generation sequencing, are challenged by high costs, time consumption, and insufficient detection of low-frequency mutations. This study proposes a novel multi-view multiple instance learning model, named MP-MIL, for non-invasively predicting PIK3CA mutation status from whole slide images (WSIs). MP-MIL effectively captures the complex spatial relationships and heterogeneity of the tumor microenvironment by fusing multi-scale features with patch sizes of$256 \times 256$and$512 \times 512$. It introducing a regional multi-head self-attention mechanism (RMSA) and a position embedding for attention-based (PEAT). Furthermore, the multi-view feature concatenation (MVC) module integrates microscopic details with macroscopic contextual information, improving the model's adaptability to heterogeneous pathological data. Experimental results on three datasets, TCGA-LUAD, TCGA-LUSC, and TCGA-BRCA, demonstrate that MP-MIL outperforms seven baseline models on most performance metrics. Ablation experiments further validated the key role of multi-view feature integration and the PEAT module in improving prediction performance. MP-MIL provides an efficient and non-invasive method for PIK3CA mutation prediction, providing important support for the advancement of precision medicine.

Detecting and segmenting cell nuclei in Hematoxylin and Eosin (H&E) stained tissue images is a critical clinical task with broad applications. However, it is a challenging problem due to variations in staining and size, overlapping boundaries, cell clustering, and the high morphological and size variability of lesion regions in medical images. Accurate segmentation in medical imaging requires precise global contour localization and careful handling of local boundaries. Existing CNN-based and Transformer-based models are often limited by high parameter counts and computational complexity, making it difficult to effectively integrate these features. To address this challenge, we propose a spatially feature-aware segmentation and classification model for cell nuclei, named SpaceSeg. The Partial Gated Feed-forward Network module in SpaceSeg enhances feature representations, enabling the model to focus on key features while reducing attention to redundant information. The Spatial Attention Block enhances the spatial information of features passed to the decoder, allowing the network to automatically focus on the most critical regions in the image, especially those related to nuclear boundaries. The Partial Gated CNN module more accurately fuses low-level and high-level features, helping the model learn finer semantic information. Experimental results on the PanNuke and MoNuSeg datasets demonstrate that SpaceSeg outperforms all existing state-of-the-art models.

Whole Slide Images (WSI) play a vital role in disease diagnosis, especially for complex diseases like tumors. Although deep learning, particularly Multi-Instance Learning, has advanced pathological image analysis, challenges remain: (1) the high resolution of WSIs and limited annotations hinder supervised learning; (2) fixed data distributions lead to overfitting and poor generalization. To address these issues, we propose BTDA-MIL, a novel MIL model based on Bag Transformation Data Augmentation. BTDA-MIL designs two transformation strategies-bag merging and bag sampling-to dynamically expand and diversify training data. The model adopts a teacher-student framework, where the teacher's learned knowledge guides pseudo-bag construction, enhancing the student's classification ability. This dynamic augmentation exposes the model to varying data distributions, reducing overfitting risks. Experiments on CPTAC and TCGA-RCC datasets demonstrate that BTDA-MIL outperforms state-of-the-art MIL methods, achieving superior WSI classification performance.

Lung cancer remains a leading cause of cancer mortality globally. Recently, targeted therapies have significantly improved clinical outcomes in lung cancer patients, making accurate identification of driver gene mutations crucial for precision treatment. Artificial intelligence approaches leveraging routinely acquired whole-slide histopathology images (WSIs) offer a promising and cost-effective means for molecular biomarker prediction, potentially enhancing clinical decision-making. However, mutation prediction from WSIs faces substantial technical challenges, including high data dimensionality, weakly supervised labels, and severe class imbalance inherent in clinical datasets. To address these issues, we propose GEMIL, a novel multipleinstance learning (MIL) model. GEMIL features a hierarchical attention-based encoder that employs deep non-linear projections and structured regularization to learn discriminative patch-level representations. These features are then aggregated by a querydriven decoder, which efficiently consolidates instance information into a robust slide-level prediction. We validated GEMIL through extensive experiments on the large-scale PathGene dataset, with further evaluation on an independent TCGA cohort. The results demonstrate that GEMIL consistently outperforms state-of-the-art MIL methods across multiple gene mutation prediction tasks (TP53, EGFR, etc.), improving the average accuracy by 2.8% and the F1-score by 3.1%. Consequently, GEMIL provides a robust and generalizable computational tool for WSI-based biomarker prediction, holding significant potential to advance precision oncology.

Spread through air spaces (STAS) represents a newly identified aggressive pattern in lung cancer, which is known to be associated with adverse prognostic factors and complex pathological features. Pathologists currently rely on time-consuming manual assessments, which are highly subjective and prone to variation. This highlights the urgent need for automated and precise diagnostic solutions. 2,970 lung cancer tissue slides are comprised from multiple centers, re-diagnosed them, and constructed and publicly released three lung cancer STAS datasets: STAS-CSU (hospital), STAS-TCGA, and STAS-CPTAC. All STAS datasets provide corresponding pathological feature diagnoses and related clinical data. To address the bias, sparse and heterogeneous nature of STAS, we propose an scale-aware multiple instance learning(SMILE) method for STAS diagnosis of lung cancer. By introducing a scale-adaptive attention mechanism, the SMILE can adaptively adjust high-attention instances, reducing over-reliance on local regions and promoting consistent detection of STAS lesions. Extensive experiments show that SMILE achieved competitive diagnostic results on STAS-CSU, diagnosing 251 and 319 STAS samples in CPTAC and TCGA, respectively, surpassing clinical average AUC. The 11 open baseline results are the first to be established for STAS research, laying the foundation for the future expansion, interpretability, and clinical integration of computational pathology technologies. The datasets and code are available at https://github.com/panliangrui/IJCAI25.

Exploring how robots impact human cognition and emotions has become increasingly important as robots gradually become ubiquitous in our lives. In this study, we investigate the impact of robotic presence on human cognition and emotion by examining various robot parameters such as anthropomorphism, number of robots, and multi-robot motion patterns. 16 participants completed two cognitive tasks in the presence of anthropomorphic and non-anthropomorphic robots, alone, and with a human nearby. The non-anthropomorphic robot conditions were further varied in the number of robots and their motion patterns. We find that increasing the number of non-anthropomorphic robots generally leads to slower performance, but coordinated patterned motions can lower the completion time compared to random movements. An anthropomorphic robot induces an increased level of feelings of being judged compared to a non-anthropomorphic robot. These findings provide preliminary insights into how designers or users can purposefully integrate robots into our environment by understanding the effects of anthropomorphism, number of robots, and multi-robot motion patterns on human cognition and emotion.