Hsing-Kuo Pao

Zolnamar Dorjsembe, Hung-Yi Chen, Furen Xiao et al.

MRI provides superior soft tissue contrast without ionizing radiation; however, the absence of electron density information limits its direct use for dose calculation. As a result, current radiotherapy workflows rely on combined MRI and CT acquisitions, increasing registration uncertainty and procedural complexity. Synthetic CT generation enables MRI only planning but remains challenging due to nonlinear MRI-CT relationships and anatomical variability. We propose Parallel Swin Transformer-Enhanced Med2Transformer, a 3D architecture that integrates convolutional encoding with dual Swin Transformer branches to model both local anatomical detail and long-range contextual dependencies. Multi-scale shifted window attention with hierarchical feature aggregation improves anatomical fidelity. Experiments on public and clinical datasets demonstrate higher image similarity and improved geometric accuracy compared with baseline methods. Dosimetric evaluation shows clinically acceptable performance, with a mean target dose error of 1.69%. Code is available at: https://github.com/mobaidoctor/med2transformer.

Zolnamar Dorjsembe, Hsing-Kuo Pao, Furen Xiao

This study introduces Polyp-DDPM, a diffusion-based method for generating realistic images of polyps conditioned on masks, aimed at enhancing the segmentation of gastrointestinal (GI) tract polyps. Our approach addresses the challenges of data limitations, high annotation costs, and privacy concerns associated with medical images. By conditioning the diffusion model on segmentation masks-binary masks that represent abnormal areas-Polyp-DDPM outperforms state-of-the-art methods in terms of image quality (achieving a Frechet Inception Distance (FID) score of 78.47, compared to scores above 83.79) and segmentation performance (achieving an Intersection over Union (IoU) of 0.7156, versus less than 0.6694 for synthetic images from baseline models and 0.7067 for real data). Our method generates a high-quality, diverse synthetic dataset for training, thereby enhancing polyp segmentation models to be comparable with real images and offering greater data augmentation capabilities to improve segmentation models. The source code and pretrained weights for Polyp-DDPM are made publicly available at https://github.com/mobaidoctor/polyp-ddpm.

Zolnamar Dorjsembe, Hsing-Kuo Pao, Sodtavilan Odonchimed et al.

Artificial intelligence (AI) in healthcare, especially in medical imaging, faces challenges due to data scarcity and privacy concerns. Addressing these, we introduce Med-DDPM, a diffusion model designed for 3D semantic brain MRI synthesis. This model effectively tackles data scarcity and privacy issues by integrating semantic conditioning. This involves the channel-wise concatenation of a conditioning image to the model input, enabling control in image generation. Med-DDPM demonstrates superior stability and performance compared to existing 3D brain imaging synthesis methods. It generates diverse, anatomically coherent images with high visual fidelity. In terms of dice score accuracy in the tumor segmentation task, Med-DDPM achieves 0.6207, close to the 0.6531 accuracy of real images, and outperforms baseline models. Combined with real images, it further increases segmentation accuracy to 0.6675, showing the potential of our proposed method for data augmentation. This model represents the first use of a diffusion model in 3D semantic brain MRI synthesis, producing high-quality images. Its semantic conditioning feature also shows potential for image anonymization in biomedical imaging, addressing data and privacy issues. We provide the code and model weights for Med-DDPM on our GitHub repository (https://github.com/mobaidoctor/med-ddpm/) to support reproducibility.

Yen-Chia Chen, Hsing-Kuo Pao, Hanjuan Huang

Deep learning models are widely used across computer vision and other domains. When working on the model induction, selecting the right architecture for a given dataset often relies on repetitive trial-and-error procedures. This procedure is time-consuming, resource-intensive, and difficult to automate. While previous work has explored performance prediction using partial training or complex simulations, these methods often require significant computational overhead or lack generalizability. In this work, we propose an alternative approach: a lightweight, two-stage framework that can estimate model performance before training given the understanding of the dataset and the focused deep model structures. The first stage predicts a baseline based on the analysis of some measurable properties of the dataset, while the second stage adjusts the estimation with additional information on the model's architectural and hyperparameter details. The setup allows the framework to generalize across datasets and model types. Moreover, we find that some of the underlying features used for prediction - such as dataset variance - can offer practical guidance for model selection, and can serve as early indicators of data quality. As a result, the framework can be used not only to forecast model performance, but also to guide architecture choices, inform necessary preprocessing procedures, and detect potentially problematic datasets before training begins.

Euhid Aman, Esteban Carlin, Hsing-Kuo Pao et al.

Cross-attention transformers and other multimodal vision-language models excel at grounding and generation; however, their extensive, full-precision backbones make it challenging to deploy them on edge devices. Memory-augmented architectures enhance the utilization of past context; however, most works rarely pair them with aggressive edge-oriented quantization. We introduce BitMar, a quantized multimodal transformer that proposes an external human-like episodic memory for effective image-text generation on hardware with limited resources. BitMar utilizes 1.58-bit encoders, one for text (BitNet-style) and one for vision (DiNOv2-based), to create compact embeddings that are combined and used to query a fixed-size key-value episodic memory. During vector retrieval, the BitNet decoder applies per-layer conditioning, which increases the contextual relevance of generated content. The decoder also employs attention sinks with a sliding-window mechanism to process long or streaming inputs under tight memory budgets. The combination of per-layer conditioning and sliding-window attention achieves a strong quality-speed trade-off, delivering competitive captioning and multimodal understanding at low latency with a small model footprint. These characteristics make BitMar well-suited for edge deployment.

Hanjuan Huang, Hao-Jia Song, Hsing-Kuo Pao

We surely enjoy the larger the better models for their superior performance in the last couple of years when both the hardware and software support the birth of such extremely huge models. The applied fields include text mining and others. In particular, the success of LLMs on text understanding and text generation draws attention from researchers who have worked on NLP and related areas for years or even decades. On the side, LLMs may suffer from problems like model overfitting, hallucination, and device limitation to name a few. In this work, we suggest a model pruning technique specifically focused on LLMs. The proposed methodology emphasizes the explainability of deep learning models. By having the theoretical foundation, we obtain a trustworthy deep model so that huge models with a massive number of model parameters become not quite necessary. A mutual information-based estimation is adopted to find neurons with redundancy to eliminate. Moreover, an estimator with well-tuned parameters helps to find precise estimation to guide the pruning procedure. At the same time, we also explore the difference between pruning on large-scale models vs. pruning on small-scale models. The choice of pruning criteria is sensitive in small models but not for large-scale models. It is a novel finding through this work. Overall, we demonstrate the superiority of the proposed model to the state-of-the-art models.