Meta-learning, or "learning to learn," aims to enable models to quickly adapt to new tasks with minimal data. While traditional methods like Model-Agnostic Meta-Learning (MAML) optimize parameters in Euclidean space, they often struggle to capture complex learning dynamics, particularly in few-shot learning scenarios. To address this limitation, we propose Stiefel-MAML, which integrates Riemannian geometry by optimizing within the Stiefel manifold, a space that naturally enforces orthogonality constraints. By leveraging the geometric structure of the Stiefel manifold, we improve parameter expressiveness and enable more efficient optimization through Riemannian gradient calculations and retraction operations. We also introduce a novel kernel-based loss function defined on the Stiefel manifold, further enhancing the model’s ability to explore the parameter space. Experimental results on benchmark datasets—including Omniglot, Mini-ImageNet, FC-100, and CUB—demonstrate that Stiefel-MAML consistently outperforms traditional MAML, achieving superior performance across various few-shot learning tasks. Our findings highlight the potential of Riemannian geometry to enhance meta-learning, paving the way for future research on optimizing over different geometric structures.

Accurate survival prediction using multimodal magnetic resonance imaging (MRI) plays a crucial role in clinical decision-making for patients with glioblastoma (GBM). In this work, we propose a multimodal framework, GlioSurvNet, that integrates deep learning features extracted from Swin UNETR and clinical variables to predict patient survival. Our framework makes use of multiple MRI sequences, including T1, T1 with contrast enhancement, T2-weighted, and FLAIR MRI, to capture diverse tumor characteristics. The Swin UNETR architecture simultaneously carries out tumor segmentation and extracts hierarchical features from multimodal MRI data. These deep learning features are then combined with clinical variables, which are input into a multi-layer perceptron network to yield survival probabilities. We evaluated our framework on a cohort of 287 patients from two independent databases, UPENN-GBM and UCSF-PDGM, demonstrating superior survival prediction performance when compared with existing methods. Our framework achieved a time-dependent concordance index of 0.693 and an integrated brier score of 0.14 with improved risk stratification. GlioSurvNet offers a robust tool for personalized prognosis and treatment planning in GBM patients.

Medical datasets are often large and contain sensitive information, presenting significant challenges for data sharing and storage. To address these issues, this paper introduces a novel method called Fused Matching Distillation (FMD), which combines multiple dataset distillation techniques to achieve both data compression and enhanced privacy. FMD synthesizes representative subsets that capture the essential information from the original dataset while anonymizing sensitive details during the distillation process. The proposed approach integrates two complementary methods: parameter matching,a technique that aligns the training trajectories of a teacher network trained on real data with those of a student network trained on synthetic data, and feature distribution matching, which ensures that the synthetic dataset closely approximates the feature distribution of the original data. By fusing these techniques, FMD maximizes the information density within each pixel of the distilled dataset, achieving a balance between compression and performance. Experimental evaluations on medical datasets, including COVID chest X-ray and Pancreas cancer CT, demonstrate that FMD achieves superior accuracy and privacy compared to existing methods. Furthermore, the proposed method is evaluated using metrics for both model performance and anonymity, showing that FMD not only maintains high diagnostic accuracy but also effectively anonymizes the data. This makes FMD a promising tool for secure and efficient medical data sharing. The code is available at the provided link11https://github.com/juheonewha/FMD.git.

In computed tomography (CT) imaging, optimizing the balance between radiation dose and image quality is crucial due to the potentially harmful effects of radiation on patients. Although subjective assessments by radiologists are considered the gold standard in medical imaging, these evaluations can be time-consuming and costly. Thus, objective methods, such as the peak signal-to-noise ratio and structural similarity index measure, are often employed as alternatives. However, these metrics, initially developed for natural images, may not fully encapsulate the radiologists' assessment process. Consequently, interest in developing deep learning-based image quality assessment (IQA) methods that more closely align with radiologists' perceptions is growing. A significant barrier to this development has been the absence of open-source datasets and benchmark models specific to CT IQA. Addressing these challenges, we organized the Low-dose Computed Tomography Perceptual Image Quality Assessment Challenge in conjunction with the Medical Image Computing and Computer Assisted Intervention 2023. This event introduced the first open-source CT IQA dataset, consisting of 1,000 CT images of various quality, annotated with radiologists' assessment scores. As a benchmark, this challenge offers a comprehensive analysis of six submitted methods, providing valuable insight into their performance. This paper presents a summary of these methods and insights. This challenge underscores the potential for developing no-reference IQA methods that could exceed the capabilities of full-reference IQA methods, making a significant contribution to the research community with this novel dataset. The dataset is accessible at https://zenodo.org/records/7833096.

Accurate prediction of peptide-HLA class I binding is crucial for immunotherapy and vaccine development, but existing methods often struggle to capture the intricate biological relationships between peptides and diverse HLA alleles. Here, we introduce SeqDA-HLA, a pan-specific prediction model that combines language model-based embeddings (ELMo) with a dual attention mechanism-self-aligned cross-attention and self-attention-to capture rich contextual features and pairwise interactions. Evaluations against 14 state-of-the-art methods on multiple benchmark datasets demonstrate that SeqDA-HLA consistently outperforms competing approaches, achieving an AUC value up to 0.9856 and accuracy as high as 0.9408. Notably, SeqDA-HLA maintains robust performance across peptide lengths (8-14) and HLA alleles, showcasing its generalizability. Beyond predictive accuracy, SeqDA-HLA offers interpretability by highlighting essential anchor residues and revealing key binding motifs, thereby aligning with experimentally validated biological insights. As a further demonstration of practical impact, we fine-tune SeqDA-HLA on an Influenza virus dataset, successfully predicting binding changes induced by single amino acid mutations. Overall, SeqDA-HLA serves as a powerful and interpretable tool for peptide-HLA binding prediction, with potential applications in epitope-based vaccine design and precision immunotherapy.

The integration of deep learning technologies in medical imaging aims to enhance the efficiency and accuracy of cancer diagnosis, particularly for pancreatic and breast cancers, which present significant diagnostic challenges due to their high mortality rates and complex imaging characteristics. This paper introduces Diffusion-Driven Diagnosis (D-Cube), a novel approach that leverages hyper-features from a diffusion model combined with contrastive learning to improve cancer diagnosis. D-Cube employs advanced feature selection techniques that utilize the robust representational capabilities of diffusion models, enhancing classification performance on medical datasets under challenging conditions such as data imbalance and limited sample availability. The feature selection process optimizes the extraction of clinically relevant features, significantly improving classification accuracy and demonstrating resilience in imbalanced and limited data scenarios. Experimental results validate the effectiveness of D-Cube across multiple medical imaging modalities, including CT, MRI, and X-ray, showing superior performance compared to existing baseline models. D-Cube represents a new strategy in cancer detection, employing advanced deep learning techniques to achieve state-of-the-art diagnostic accuracy and efficiency. The code is available at the provided link11.https://github.com/medical-ai-cv/D-Cube.git.

Recently, transformer-based architectures have been shown to outperform classic convolutional architectures and have rapidly been established as state-of-the-art models for many medical vision tasks. Their superior performance can be explained by their ability to capture long-range dependencies of their multi-head self-attention mechanism. However, they tend to overfit on small- or even medium-sized datasets because of their weak inductive bias. As a result, they require massive, labeled datasets, which are expensive to obtain, especially in the medical domain. This motivated us to explore unsupervised semantic feature learning without any form of annotation. In this work, we aimed to learn semantic features in a self-supervised manner by training transformer-based models to segment the numerical signals of geometric shapes inserted on original computed tomography (CT) images. Moreover, we developed a Convolutional Pyramid vision Transformer (CPT) that leverages multi-kernel convolutional patch embedding and local spatial reduction in each of its layer to generate multi-scale features, capture local information, and reduce computational cost. Using these approaches, we were able to noticeably outperformed state-of-the-art deep learning-based segmentation or classification models of liver cancer CT datasets of 5,237 patients, the pancreatic cancer CT datasets of 6,063 patients, and breast cancer MRI dataset of 127 patients.

In this paper, we consider the use of prior knowledge within neural networks. In particular, we investigate the effect of a known transform within the mapping from input data space to the output domain. We demonstrate that use of known transforms is able to change maximal error bounds and that these are additive for the entire sequence of transforms. In order to explore the effect further, we consider the problem of X-ray material decomposition as an example to incorporate additional prior knowledge. We demonstrate that inclusion of a non-linear function known from the physical properties of the system is able to reduce prediction errors therewith improving prediction quality from SSIM values of 0.54 to 0.88. This approach is applicable to a wide set of applications in physics and signal processing that provide prior knowledge on such transforms. Also maximal error estimation and network understanding could be facilitated using this novel concept of precision learning.