Dejia Cai

Qian Zeng, Yihui Wang, Shu Yang et al.

Whole-slide images (WSIs) are an important data modality in computational pathology, yet their gigapixel resolution and lack of fine-grained annotations challenge conventional deep learning models. Multiple instance learning (MIL) offers a solution by treating each WSI as a bag of patch-level instances, but effectively modeling ultra-long sequences with rich spatial context remains difficult. Recently, Mamba has emerged as a promising alternative for long sequence learning, scaling linearly to thousands of tokens. However, despite its efficiency, it still suffers from limited spatial context modeling and memory decay, constraining its effectiveness to WSI analysis. To address these limitations, we propose MambaMIL+, a new MIL framework that explicitly integrates spatial context while maintaining long-range dependency modeling without memory forgetting. Specifically, MambaMIL+ introduces 1) overlapping scanning, which restructures the patch sequence to embed spatial continuity and instance correlations; 2) a selective stripe position encoder (S2PE) that encodes positional information while mitigating the biases of fixed scanning orders; and 3) a contextual token selection (CTS) mechanism, which leverages supervisory knowledge to dynamically enlarge the contextual memory for stable long-range modeling. Extensive experiments on 20 benchmarks across diagnostic classification, molecular prediction, and survival analysis demonstrate that MambaMIL+ consistently achieves state-of-the-art performance under three feature extractors (ResNet-50, PLIP, and CONCH), highlighting its effectiveness and robustness for large-scale computational pathology

Dejia Cai, Yao Ran, Kun Yang et al.

High-Intensity Focused Ultrasound (HIFU) is a non-invasive therapeutic technique widely used for treating various diseases. However, the success and safety of HIFU treatments depend on real-time monitoring, which is often hindered by interference when using ultrasound to guide HIFU treatment. To address these challenges, we developed HIFU-ILDiff, a novel deep learning-based approach leveraging latent diffusion models to suppress HIFU-induced interference in ultrasound images. The HIFU-ILDiff model employs a Vector Quantized Variational Autoencoder (VQ-VAE) to encode noisy ultrasound images into a lower-dimensional latent space, followed by a latent diffusion model that iteratively removes interference. The denoised latent vectors are then decoded to reconstruct high-resolution, interference-free ultrasound images. We constructed a comprehensive dataset comprising 18,872 image pairs from in vitro phantoms, ex vivo tissues, and in vivo animal data across multiple imaging modalities and HIFU power levels to train and evaluate the model. Experimental results demonstrate that HIFU-ILDiff significantly outperforms the commonly used Notch Filter method, achieving a Structural Similarity Index (SSIM) of 0.796 and Peak Signal-to-Noise Ratio (PSNR) of 23.780 compared to SSIM of 0.443 and PSNR of 14.420 for the Notch Filter under in vitro scenarios. Additionally, HIFU-ILDiff achieves real-time processing at 15 frames per second, markedly faster than the Notch Filter's 5 seconds per frame. These findings indicate that HIFU-ILDiff is able to denoise HIFU interference in ultrasound guiding images for real-time monitoring during HIFU therapy, which will greatly improve the treatment precision in current clinical applications.