RepLoRA: Reparameterizing Low-Rank Adaptation via the Perspective of Mixture of Experts

Low-rank adaptation (LoRA) has emerged as a powerful method for fine-tuning large-scale foundation models. Despite its popularity, the theoretical understanding of LoRA has remained limited. This paper presents a theoretical analysis of LoRA by examining its connection to the Mixture of Experts models. Under this framework, we show that simple reparameterizations of the LoRA matrices can notably accelerate the low-rank matrix estimation process. In particular, we prove that reparameterization can reduce the data needed to achieve a desired estimation error from an exponential to a polynomial scale. Motivated by this insight, we propose Reparameterized Low-rank Adaptation (RepLoRA), which incorporates lightweight MLPs to reparameterize the LoRA matrices. Extensive experiments across multiple domains demonstrate that RepLoRA consistently outperforms vanilla LoRA. Notably, with limited data, RepLoRA surpasses LoRA by a margin of up to 40.0% and achieves LoRA's performance with only 30.0% of the training data, highlighting both the theoretical and empirical robustness of our PEFT method.

Exploring AI-based System Design for Pixel-level Protected Health Information Detection in Medical Images

De-identification of medical images is a critical step to ensure privacy during data sharing in research and clinical settings. The initial step in this process involves detecting Protected Health Information (PHI), which can be found in image metadata or imprinted within image pixels. Despite the importance of such systems, there has been limited evaluation of existing AI-based solutions, creating barriers to the development of reliable and robust tools. In this study, we present an AI-based pipeline for PHI detection, comprising three key components: text detection, text extraction, and analysis of PHI content in medical images. By experimenting with exchanging roles of vision and language models within the pipeline, we evaluate the performance and recommend the best setup for the PHI detection task.

Leveraging Hierarchical Taxonomies in Prompt-based Continual Learning

Drawing inspiration from human learning behaviors, this work proposes a novel approach to mitigate catastrophic forgetting in Prompt-based Continual Learning models by exploiting the relationships between continuously emerging class data. We find that applying human habits of organizing and connecting information can serve as an efficient strategy when training deep learning models. Specifically, by building a hierarchical tree structure based on the expanding set of labels, we gain fresh insights into the data, identifying groups of similar classes could easily cause confusion. Additionally, we delve deeper into the hidden connections between classes by exploring the original pretrained model's behavior through an optimal transport-based approach. From these insights, we propose a novel regularization loss function that encourages models to focus more on challenging knowledge areas, thereby enhancing overall performance. Experimentally, our method demonstrated significant superiority over the most robust state-of-the-art models on various benchmarks.

Improving Generalization with Flat Hilbert Bayesian Inference

We introduce Flat Hilbert Bayesian Inference (FHBI), an algorithm designed to enhance generalization in Bayesian inference. Our approach involves an iterative two-step procedure with an adversarial functional perturbation step and a functional descent step within the reproducing kernel Hilbert spaces. This methodology is supported by a theoretical analysis that extends previous findings on generalization ability from finite-dimensional Euclidean spaces to infinite-dimensional functional spaces. To evaluate the effectiveness of FHBI, we conduct comprehensive comparisons against seven baseline methods on the VTAB-1K benchmark, which encompasses 19 diverse datasets across various domains with diverse semantics. Empirical results demonstrate that FHBI consistently outperforms the baselines by notable margins, highlighting its practical efficacy.

Five Pitfalls When Assessing Synthetic Medical Images with Reference Metrics

Reference metrics have been developed to objectively and quantitatively compare two images. Especially for evaluating the quality of reconstructed or compressed images, these metrics have shown very useful. Extensive tests of such metrics on benchmarks of artificially distorted natural images have revealed which metric best correlate with human perception of quality. Direct transfer of these metrics to the evaluation of generative models in medical imaging, however, can easily lead to pitfalls, because assumptions about image content, image data format and image interpretation are often very different. Also, the correlation of reference metrics and human perception of quality can vary strongly for different kinds of distortions and commonly used metrics, such as SSIM, PSNR and MAE are not the best choice for all situations. We selected five pitfalls that showcase unexpected and probably undesired reference metric scores and discuss strategies to avoid them.

Agnostic Sharpness-Aware Minimization

Sharpness-aware minimization (SAM) has been instrumental in improving deep neural network training by minimizing both the training loss and the sharpness of the loss landscape, leading the model into flatter minima that are associated with better generalization properties. In another aspect, Model-Agnostic Meta-Learning (MAML) is a framework designed to improve the adaptability of models. MAML optimizes a set of meta-models that are specifically tailored for quick adaptation to multiple tasks with minimal fine-tuning steps and can generalize well with limited data. In this work, we explore the connection between SAM and MAML, particularly in terms of enhancing model generalization. We introduce Agnostic-SAM, a novel approach that combines the principles of both SAM and MAML. Agnostic-SAM adapts the core idea of SAM by optimizing the model towards wider local minima using training data, while concurrently maintaining low loss values on validation data. By doing so, it seeks flatter minima that are not only robust to small perturbations but also less vulnerable to data distributional shift problems. Our experimental results demonstrate that Agnostic-SAM significantly improves generalization over baselines across a range of datasets and under challenging conditions such as noisy labels and data limitation.

Content-Based Image Retrieval for Multi-Class Volumetric Radiology Images: A Benchmark Study

While content-based image retrieval (CBIR) has been extensively studied in natural image retrieval, its application to medical images presents ongoing challenges, primarily due to the 3D nature of medical images. Recent studies have shown the potential use of pre-trained vision embeddings for CBIR in the context of radiology image retrieval. However, a benchmark for the retrieval of 3D volumetric medical images is still lacking, hindering the ability to objectively evaluate and compare the efficiency of proposed CBIR approaches in medical imaging. In this study, we extend previous work and establish a benchmark for region-based and multi-organ retrieval using the TotalSegmentator dataset (TS) with detailed multi-organ annotations. We benchmark embeddings derived from pre-trained supervised models on medical images against embeddings derived from pre-trained unsupervised models on non-medical images for 29 coarse and 104 detailed anatomical structures in volume and region levels. We adopt a late interaction re-ranking method inspired by text matching for image retrieval, comparing it against the original method proposed for volume and region retrieval achieving retrieval recall of 1.0 for diverse anatomical regions with a wide size range. The findings and methodologies presented in this paper provide essential insights and benchmarks for the development and evaluation of CBIR approaches in the context of medical imaging.

Similarity Metrics for MR Image-To-Image Translation

Image-to-image translation can create large impact in medical imaging, i.e. if images of a patient can be translated to another modality, type or sequence for better diagnosis. However, these methods must be validated by human reader studies, which are costly and restricted to small samples. Automatic evaluation of large samples to pre-evaluate and continuously improve methods before human validation is needed. In this study, we give an overview of reference and non-reference metrics for image synthesis assessment and investigate the ability of nine metrics, that need a reference (SSIM, MS-SSIM, PSNR, MSE, NMSE, MAE, LPIPS, NMI and PCC) and three non-reference metrics (BLUR, MSN, MNG) to detect 11 kinds of distortions in MR images from the BraSyn dataset. In addition we test a downstream segmentation metric and the effect of three normalization methods (Minmax, cMinMax and Zscore). Although PSNR and SSIM are frequently used to evaluate generative models for image-to-image-translation tasks in the medical domain, they show very specific shortcomings. SSIM ignores blurring but is very sensitive to intensity shifts in unnormalized MR images. PSNR is even more sensitive to different normalization methods and hardly measures the degree of distortions. Further metrics, such as LPIPS, NMI and DICE can be very useful to evaluate other similarity aspects. If the images to be compared are misaligned, most metrics are flawed. By carefully selecting and reasonably combining image similarity metrics, the training and selection of generative models for MR image synthesis can be improved. Many aspects of their output can be validated before final and costly evaluation by trained radiologists is conducted.

Benchmarking Pretrained Vision Embeddings for Near- and Duplicate Detection in Medical Images

Near- and duplicate image detection is a critical concern in the field of medical imaging. Medical datasets often contain similar or duplicate images from various sources, which can lead to significant performance issues and evaluation biases, especially in machine learning tasks due to data leakage between training and testing subsets. In this paper, we present an approach for identifying near- and duplicate 3D medical images leveraging publicly available 2D computer vision embeddings. We assessed our approach by comparing embeddings extracted from two state-of-the-art self-supervised pretrained models and two different vector index structures for similarity retrieval. We generate an experimental benchmark based on the publicly available Medical Segmentation Decathlon dataset. The proposed method yields promising results for near- and duplicate image detection achieving a mean sensitivity and specificity of 0.9645 and 0.8559, respectively.

Medical Image Retrieval Using Pretrained Embeddings

A wide range of imaging techniques and data formats available for medical images make accurate retrieval from image databases challenging. Efficient retrieval systems are crucial in advancing medical research, enabling large-scale studies and innovative diagnostic tools. Thus, addressing the challenges of medical image retrieval is essential for the continued enhancement of healthcare and research. In this study, we evaluated the feasibility of employing four state-of-the-art pretrained models for medical image retrieval at modality, body region, and organ levels and compared the results of two similarity indexing approaches. Since the employed networks take 2D images, we analyzed the impacts of weighting and sampling strategies to incorporate 3D information during retrieval of 3D volumes. We showed that medical image retrieval is feasible using pretrained networks without any additional training or fine-tuning steps. Using pretrained embeddings, we achieved a recall of 1 for various tasks at modality, body region, and organ level.