Transferring features learned from natural to medical images for classification is common. However, challenges arise due to the scarcity of certain medical image types and the feature disparities between natural and medical images. Two-step transfer learning has been recognized as a promising solution for this issue. However, choosing an appropriate intermediate domain would be critical in further improving the classification performance. In this work, we explore the effectiveness of using color fundus photographs of the diabetic retina dataset as an intermediate domain for two-step heterogeneous learning (THTL) to classify laryngeal vascular images with nine deep-learning models. Experiment results confirm that although the images in both the intermediate and target domains share vascularized characteristics, the accuracy is drastically reduced compared to one-step transfer learning, where only the last layer is fine-tuned (e.g., ResNet18 drops 14.7%, ResNet50 drops 14.8%). By analyzing the Layer Class Activation Maps (LayerCAM), we uncover a novel finding that the prevalent radial vascular pattern in the intermediate domain prevents learning the features of twisted and tangled vessels that distinguish the malignant class in the target domain. To address the performance drop, we propose the Step-Wise Fine-Tuning (SWFT) method on ResNet in the second step of THTL, resulting in substantial accuracy improvements. Compared to THTL's second step, where only the last layer is fine-tuned, accuracy increases by 26.1% for ResNet18 and 20.4% for ResNet50. Additionally, compared to training from scratch, using ImageNet as the source domain could slightly improve classification performance for laryngeal vascular, but the differences are insignificant.
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Automatic prompt optimization is an important approach to improving the performance of large language models (LLMs). Recent research demonstrates the potential of using LLMs as prompt optimizers, which can generate improved task prompts via iterative refinement. In this paper, we propose a novel perspective to investigate the design of LLM-based prompt optimizers, by drawing an analogy with gradient-based model optimizers. To connect these two approaches, we identify two pivotal factors in model parameter learning: update direction and update method. Focused on the two aspects, we borrow the theoretical framework and learning methods from gradient-based optimization to design improved strategies for LLM-based prompt optimizers. By systematically analyzing a rich set of improvement strategies, we further develop a capable Gradient-inspired LLM-based Prompt Optimizer called GPO. At each step, it first retrieves relevant prompts from the optimization trajectory as the update direction. Then, it utilizes the generation-based refinement strategy to perform the update, while controlling the edit distance through a cosine-based decay strategy. Extensive experiments demonstrate the effectiveness and efficiency of GPO. In particular, GPO brings an additional improvement of up to 56.8% on Big-Bench Hard and 55.3% on MMLU compared to baseline methods.
Alcohol consumption has been shown to influence cardiovascular mechanisms in humans, leading to observable alterations in the plasma metabolomic profile. Regression models are commonly employed to investigate these effects, treating metabolomics features as the outcomes and alcohol intake as the exposure. Given the latent dependence structure among the numerous metabolomic features (e.g., co-expression networks with interconnected modules), modeling this structure is crucial for accurately identifying metabolomic features associated with alcohol intake. However, integrating dependence structures into regression models remains difficult in both estimation and inference procedures due to their large or high dimensionality. To bridge this gap, we propose an innovative multivariate regression model that accounts for correlations among outcome features by incorporating an interconnected community structure. Furthermore, we derive closed-form and likelihood-based estimators, accompanied by explicit exact and explicit asymptotic covariance matrix estimators, respectively. Simulation analysis demonstrates that our approach provides accurate estimation of both dependence and regression coefficients, and enhances sensitivity while maintaining a well-controlled discovery rate, as evidenced through benchmarking against existing regression models. Finally, we apply our approach to assess the impact of alcohol intake on $249$ metabolomic biomarkers measured using nuclear magnetic resonance spectroscopy. The results indicate that alcohol intake can elevate high-density lipoprotein levels by enhancing the transport rate of Apolipoproteins A1.
Two-dimensional (2D) freehand ultrasonography is one of the most commonly used medical imaging modalities, particularly in obstetrics and gynaecology. However, it only captures 2D cross-sectional views of inherently 3D anatomies, losing valuable contextual information. As an alternative to requiring costly and complex 3D ultrasound scanners, 3D volumes can be constructed from 2D scans using machine learning. However this usually requires long computational time. Here, we propose RapidVol: a neural representation framework to speed up slice-to-volume ultrasound reconstruction. We use tensor-rank decomposition, to decompose the typical 3D volume into sets of tri-planes, and store those instead, as well as a small neural network. A set of 2D ultrasound scans, with their ground truth (or estimated) 3D position and orientation (pose) is all that is required to form a complete 3D reconstruction. Reconstructions are formed from real fetal brain scans, and then evaluated by requesting novel cross-sectional views. When compared to prior approaches based on fully implicit representation (e.g. neural radiance fields), our method is over 3x quicker, 46% more accurate, and if given inaccurate poses is more robust. Further speed-up is also possible by reconstructing from a structural prior rather than from scratch.
Gene therapies aim to address the root causes of diseases, particularly those stemming from rare genetic defects that can be life-threatening or severely debilitating. While there has been notable progress in the development of gene therapies in recent years, understanding their long-term effectiveness remains challenging due to a lack of data on long-term outcomes, especially during the early stages of their introduction to the market. To address the critical question of estimating long-term efficacy without waiting for the completion of lengthy clinical trials, we propose a novel Bayesian framework. This framework selects pertinent data from external sources, often early-phase clinical trials with more comprehensive longitudinal efficacy data that could lead to an improved inference of the long-term efficacy outcome. We apply this methodology to predict the long-term factor IX (FIX) levels of HEMGENIX (etranacogene dezaparvovec), the first FDA-approved gene therapy to treat adults with severe Hemophilia B, in a phase 3 study. Our application showcases the capability of the framework to estimate the 5-year FIX levels following HEMGENIX therapy, demonstrating sustained FIX levels induced by HEMGENIX infusion. Additionally, we provide theoretical insights into the methodology by establishing its posterior convergence properties.
Satellite imagery is crucial for tasks like environmental monitoring and urban planning. Typically, it relies on semantic segmentation or Land Use Land Cover (LULC) classification to categorize each pixel. Despite the advancements brought about by Deep Neural Networks (DNNs), their performance in segmentation tasks is hindered by challenges such as limited availability of labeled data, class imbalance and the inherent variability and complexity of satellite images. In order to mitigate those issues, our study explores the effectiveness of a Cut-and-Paste augmentation technique for semantic segmentation in satellite images. We adapt this augmentation, which usually requires labeled instances, to the case of semantic segmentation. By leveraging the connected components in the semantic segmentation labels, we extract instances that are then randomly pasted during training. Using the DynamicEarthNet dataset and a U-Net model for evaluation, we found that this augmentation significantly enhances the mIoU score on the test set from 37.9 to 44.1. This finding highlights the potential of the Cut-and-Paste augmentation to improve the generalization capabilities of semantic segmentation models in satellite imagery.
Implicit reconstruction of ESDF (Euclidean Signed Distance Field) involves training a neural network to regress the signed distance from any point to the nearest obstacle, which has the advantages of lightweight storage and continuous querying. However, existing algorithms usually rely on conflicting raw observations as training data, resulting in poor map performance. In this paper, we propose LGSDF, an ESDF continual Global learning algorithm aided by Local updating. At the front end, axis-aligned grids are dynamically updated by pre-processed sensor observations, where incremental fusion alleviates estimation error caused by limited viewing directions. At the back end, a randomly initialized implicit ESDF neural network performs continual self-supervised learning guided by these grids to generate smooth and continuous maps. The results on multiple scenes show that LGSDF can construct more accurate ESDF maps and meshes compared with SOTA (State Of The Art) explicit and implicit mapping algorithms. The source code of LGSDF is publicly available at //github.com/BIT-DYN/LGSDF.
The widespread use of Artificial Intelligence-based tools in the healthcare sector raises many ethical and legal problems, one of the main reasons being their black-box nature and therefore the seemingly opacity and inscrutability of their characteristics and decision-making process. Literature extensively discusses how this can lead to phenomena of over-reliance and under-reliance, ultimately limiting the adoption of AI. We addressed these issues by building a theoretical framework based on three concepts: Feature Importance, Counterexample Explanations, and Similar-Case Explanations. Grounded in the literature, the model was deployed within a case study in which, using a participatory design approach, we designed and developed a high-fidelity prototype. Through the co-design and development of the prototype and the underlying model, we advanced the knowledge on how to design AI-based systems for enabling complementarity in the decision-making process in the healthcare domain. Our work aims at contributing to the current discourse on designing AI systems to support clinicians' decision-making processes.
Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES'22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. Through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model achieved superior ischemic lesion detection and segmentation accuracy on our internal test set compared to individual algorithms. This accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers. Notably, in a Turing-like test, neuroradiologists consistently preferred the algorithm's segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model's generalizability. The algorithm's outputs also demonstrated strong correlations with clinical scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived results, underlining its clinical relevance. This study offers two key findings. First, we present an ensemble algorithm (//github.com/Tabrisrei/ISLES22_Ensemble) that detects and segments ischemic stroke lesions on DWI across diverse scenarios on par with expert (neuro)radiologists. Second, we show the potential for biomedical challenge outputs to extend beyond the challenge's initial objectives, demonstrating their real-world clinical applicability.
Deep neural networks (DNNs) are successful in many computer vision tasks. However, the most accurate DNNs require millions of parameters and operations, making them energy, computation and memory intensive. This impedes the deployment of large DNNs in low-power devices with limited compute resources. Recent research improves DNN models by reducing the memory requirement, energy consumption, and number of operations without significantly decreasing the accuracy. This paper surveys the progress of low-power deep learning and computer vision, specifically in regards to inference, and discusses the methods for compacting and accelerating DNN models. The techniques can be divided into four major categories: (1) parameter quantization and pruning, (2) compressed convolutional filters and matrix factorization, (3) network architecture search, and (4) knowledge distillation. We analyze the accuracy, advantages, disadvantages, and potential solutions to the problems with the techniques in each category. We also discuss new evaluation metrics as a guideline for future research.
Many tasks in natural language processing can be viewed as multi-label classification problems. However, most of the existing models are trained with the standard cross-entropy loss function and use a fixed prediction policy (e.g., a threshold of 0.5) for all the labels, which completely ignores the complexity and dependencies among different labels. In this paper, we propose a meta-learning method to capture these complex label dependencies. More specifically, our method utilizes a meta-learner to jointly learn the training policies and prediction policies for different labels. The training policies are then used to train the classifier with the cross-entropy loss function, and the prediction policies are further implemented for prediction. Experimental results on fine-grained entity typing and text classification demonstrate that our proposed method can obtain more accurate multi-label classification results.
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