Volume 45, Issue 8

Whole slide image (WSI) classification is a critical task in computational pathology and is aimed at providing automated diagnostic support through high-resolution tissue image analysis. In weakly supervised WSI classification scenarios, the main challenge concerns the traditional multiple instance learning (MIL) methods, which rely on instance-level embeddings aggregated by an attention-based pooling mechanism. These methods often depend on data-driven statistical correlations, leading to misalignments between their attention allocation schemes and histopathological diagnostic regions and reducing the resulting prediction reliability. To address this, we propose frequency-aware causal regularized multiple instance learning (FC-MIL), an innovative framework combining that combines frequency-aware attention (FAA) and causal regularization (CR). FAA extracts more granular, fine-grained histological textures by jointly modeling spatial- and frequency- domain features, whereas CR introduces feature-level counterfactual perturbations as an intervention-inspired regularizer in the latent space, encouraging the model to rely less on spurious correlations and more on invariant pathological cues. Experimental results obtained on four WSI datasets show that FC-MIL outperforms the state-of-the-art MIL methods in terms of both accuracy and interpretability. Our source code is available at https://github.com/7FFDW/FCMIL.

全切片图像（WSI）分类是计算病理学中的一项关键任务，旨在通过高分辨率组织图像分析提供自动化诊断支持。在弱监督的WSI分类场景中，主要挑战在于传统的多实例学习（MIL）方法，这些方法依赖于基于注意力的池化聚合的实例级嵌入...

3D reconstruction of the Right Ventricle (RV) from echocardiograms is crucial for accurate clinical evaluation of cardiac function. However, existing methods are hindered by the complex RV anatomy and the incomplete spatial information inherent in 2D multi-view echocardiograms. Therefore, we propose RVDeformer, a sparse point cloud-guided framework for RV 3D reconstruction. RVDeformer reformulates the reconstruction task as a mesh deformation problem, learning to deform a predefined template mesh to match the target structure under the guidance of the sparse anatomical point cloud. Specifically, this framework employs the end-to-end neural network RVDeformNet to extract the features of the point cloud and template mesh for predicting the displacement of each mesh vertex. We design a point cloud-mesh fusion module that can effectively align and fuse features from the two modalities to enhance the representation ability of the model. We conduct extensive validation on a clinical dataset of 1,278 cases and demonstrate that RVDeformer outperforms existing state-of-the-art methods, achieving a Chamfer Distance (CD) of 2.24±0.55 mm, an F1-score of 0.74±0.10 at the 3 mm threshold, and a Volumetric Similarity (VS) of 91.53±2.28%, with significant potential for clinical applications. The code is available at https://github.com/onezh95/RVDeformer.

从超声心动图中进行右心室（RV）的三维重建对于准确评估心脏功能至关重要。然而，现有方法受限于复杂的RV解剖结构以及二维多视图超声心动图固有的空间信息不完整。因此，我们提出了RVDeformer，一种稀疏点云引导的RV三维重建框架。RVDeformer重新表述了...

Following successful large-vessel recanalization via endovascular thrombectomy (EVT) for acute ischemic stroke (AIS), some patients experience a complication known as no-reflow , defined by persistent microvascular hypoperfusion that undermines tissue recovery and worsens clinical outcomes. Although prompt identification is crucial, standard clinical practice relies on perfusion magnetic resonance imaging (MRI) within 24 hours post-procedure, delaying intervention. In this work, we introduce the first-ever machine learning (ML) framework to predict no-reflow immediately after EVT by leveraging previously unexplored intra-procedural digital subtraction angiography (DSA) sequences and clinical variables. Our retrospective analysis included AIS patients treated at UCLA Medical Center (2011–2024) who achieved favorable mTICI scores (2c or 3) and underwent pre- and post-procedure MRI. No-reflow was defined as a > 15% reduction in relative cerebral blood volume or flow within the infarct core compared to the contralateral hemisphere. From DSA sequences (anteroposterior and lateral views), we extracted statistical and temporal perfusion features from the target downstream territory to train ML classifiers for predicting no-reflow. Our preliminary results demonstrate that this novel method out-performed a clinical-features baseline (AUROC: 0.9330 vs. 0.7768 (p = 0.006)), suggesting that real-time DSA perfusion dynamics may encode clinically relevant information related to microvascular integrity. This approach establishes a preliminary foundation for immediate, accurate no-reflow prediction, enabling clinicians to proactively manage high-risk patients without reliance on delayed imaging, though it warrants validation in larger, independent cohorts.

急性缺血性卒中（AIS）通过血管内血栓切除术（EVT）成功实现大血管再通后，部分患者会出现一种称为无复流（no-reflow）的并发症，其特征为持续的微血管低灌注，损害组织恢复并恶化临床结局。尽管及时识别至关重要，但标准临床实践依赖于灌注磁共振...

Developing a reliable EEG-based Brain Computer Interface (BCI) system typically requires large and diverse training datasets, but collecting sufficient data remains challenging due to subject fatigue and interindividual variability. To address these limitations, this study proposes a reinforcement learning-enhanced EEG diffusion (RLED) framework for adaptive data augmentation in endogenous EEG tasks, with a focus on motor imagery and emotion recognition. The framework integrates a reinforcement learning mechanism to dynamically regulate the diffusion training process and achieve a flexible balance among temporal, spectral, and class-related features. Experiments on four datasets demonstrate that the proposed method generates high-quality synthetic EEG signals and consistently improves classification performance. These findings show that the proposed RLED framework may serve as a promising tool for EEG data augmentation and generalization in practical BCI applications.

开发一个可靠的基于脑电图的脑机接口系统通常需要大量多样的训练数据集，但受试者疲劳和个体差异使得收集足够数据仍然具有挑战性。为了解决这些限制，本研究提出了一种强化学习增强的脑电图扩散框架，用于内源性脑电图任务中的自适应数据增强。

Low light conditions in endoscopic imaging would lead to poor visibility, reduced contrast, and increased noise, which may hinder accurate diagnosis and surgical guidance. Against this low-light endoscopic image enhancement (LLEIE) task, inspired by the remarkable performance of pretrained CLIP in downstream vision tasks, in this paper, we carefully investigate the pretrained priors of CLIP and em...

中文摘要翻译待生成

With the rapid development of pathology foundation models, there is a growing demand for efficient fine-tuning strategies tailored to downstream tasks. However, existing parameter-efficient fine-tuning approaches are largely task-agnostic and exhibit limited generalization to histopathological images, particularly for unseen cancers and stains, due to substantial stain variability and the complexity of tissue microenvironments. To address these challenges, we present Histopathology-induced Adapter (HiAdapter), which incorporates domain-specific insights into staining and imaging mechanisms of histopathology. HiAdapter reconstructs stain-invariant representations via a Stain-invariant Adapter (S-Adapter) and integrates morphological features through a Morphology-aware Adapter (M-Adapter), effectively bridging the gap between low-level optical properties and high-level tissue semantics. Additionally, we introduce a Pathology Prototypical Contrastive Loss (PPCLoss) to reduce inter-class similarity and mitigate intra-class heterogeneity, enhancing feature discriminability. Extensive experiments using three pathology foundation models (CTransPath, CONCH and UNI) across six benchmarks, including two public datasets, an osteosarcoma tissue classification dataset (56,178 patches) and a chondrosarcoma necrosis classification dataset (3,867 patches) for unseen cancers generalization, as well as an IHC-stained dataset (4,967 patches) and an HIF1A IHC-stained dataset (4,433 patches) for unseen stains generalization, demonstrate the effectiveness of HiAdapter in both efficiency and accuracy. HiAdapter achieves an average improvement of 2.15 in F1 and 1.55 in accuracy over the second-best performer, maintaining strong biological and diagnostic interpretability. External validation on an independent osteosarcoma dataset (9,535 patches) and WSI-level survival analysis (178 slides) further confirm the superior generalizability and underscore the potential for patient-level diagnosis and prognosis in clinical practice. Our code is available at HiAdapter.

中文摘要翻译待生成