Deep Learning Models For Biomedical Data Analysis

The field of biomedical data analysis is a vibrant area of research dedicated to extracting valuable insights from a wide range of biomedical data sources, including biomedical images and genomics data. The emergence of deep learning, an artificial intelligence approach, presents significant prospects for enhancing biomedical data analysis and knowledge discovery. This dissertation focused on exploring innovative deep-learning methods for biomedical image processing and gene data analysis. During the COVID-19 pandemic, biomedical imaging data, including CT scans and chest x-rays, played a pivotal role in identifying COVID-19 cases by categorizing patient chest x-ray outcomes as COVID-19-positive or negative. While supervised deep learning methods have effectively recognized COVID-19 patterns in chest x-ray datasets, the availability of annotated training data remains limited. To address this challenge, the thesis introduced a semi-supervised deep learning model named ssResNet, built upon the Residual Neural Network (ResNet) architecture. The model combines supervised and unsupervised paths, incorporating a weighted supervised loss function to manage data imbalance. The strategies to diminish prediction uncertainty in deep learning models for critical applications like medical image processing is explore. It achieves this through an ensemble deep learning model, integrating bagging deep learning and model calibration techniques. This ensemble model not only boosts biomedical image segmentation accuracy but also reduces prediction uncertainty, as validated on a comprehensive chest x-ray image segmentation dataset. Furthermore, the thesis introduced an ensemble model integrating Proformer and ensemble learning methodologies. This model constructs multiple independent Proformers for predicting gene expression, their predictions are combined through weighted averaging to generate final predictions. Experimental outcomes underscore the efficacy of this ensemble model in enhancing prediction performance across various metrics. In conclusion, this dissertation advances biomedical data analysis by harnessing the potential of deep learning techniques. It devises innovative approaches for processing biomedical images and gene data. By leveraging deep learning\u27s capabilities, this work paves the way for further progress in biomedical data analytics and its applications within clinical contexts. Index Terms- biomedical data analysis, COVID-19, deep learning, ensemble learning, gene data analytics, medical image segmentation, prediction uncertainty, Proformer, Residual Neural Network (ResNet), semi-supervised learning

Hyper-Convolution Networks for Biomedical Image Segmentation

The convolution operation is a central building block of neural network architectures widely used in computer vision. The size of the convolution kernels determines both the expressiveness of convolutional neural networks (CNN), as well as the number of learnable parameters. Increasing the network capacity to capture rich pixel relationships requires increasing the number of learnable parameters, often leading to overfitting and/or lack of robustness. In this paper, we propose a powerful novel building block, the hyper-convolution, which implicitly represents the convolution kernel as a function of kernel coordinates. Hyper-convolutions enable decoupling the kernel size, and hence its receptive field, from the number of learnable parameters. In our experiments, focused on challenging biomedical image segmentation tasks, we demonstrate that replacing regular convolutions with hyper-convolutions leads to more efficient architectures that achieve improved accuracy. Our analysis also shows that learned hyper-convolutions are naturally regularized, which can offer better generalization performance. We believe that hyper-convolutions can be a powerful building block in future neural network architectures for computer vision tasks. We provide all of our code here: https://github.com/tym002/Hyper-ConvolutionComment: WACV 202

Efficient Ultrasound Image Analysis Models with Sonographer Gaze Assisted Distillation.

Recent automated medical image analysis methods have attained state-of-the-art performance but have relied on memory and compute-intensive deep learning models. Reducing model size without significant loss in performance metrics is crucial for time and memory-efficient automated image-based decision-making. Traditional deep learning based image analysis only uses expert knowledge in the form of manual annotations. Recently, there has been interest in introducing other forms of expert knowledge into deep learning architecture design. This is the approach considered in the paper where we propose to combine ultrasound video with point-of-gaze tracked for expert sonographers as they scan to train memory-efficient ultrasound image analysis models. Specifically we develop teacher-student knowledge transfer models for the exemplar task of frame classification for the fetal abdomen, head, and femur. The best performing memory-efficient models attain performance within 5% of conventional models that are 1000× larger in size

Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation

Semantic segmentation is essentially important to biomedical image analysis. Many recent works mainly focus on integrating the Fully Convolutional Network (FCN) architecture with sophisticated convolution implementation and deep supervision. In this paper, we propose to decompose the single segmentation task into three subsequent sub-tasks, including (1) pixel-wise image segmentation, (2) prediction of the class labels of the objects within the image, and (3) classification of the scene the image belonging to. While these three sub-tasks are trained to optimize their individual loss functions of different perceptual levels, we propose to let them interact by the task-task context ensemble. Moreover, we propose a novel sync-regularization to penalize the deviation between the outputs of the pixel-wise segmentation and the class prediction tasks. These effective regularizations help FCN utilize context information comprehensively and attain accurate semantic segmentation, even though the number of the images for training may be limited in many biomedical applications. We have successfully applied our framework to three diverse 2D/3D medical image datasets, including Robotic Scene Segmentation Challenge 18 (ROBOT18), Brain Tumor Segmentation Challenge 18 (BRATS18), and Retinal Fundus Glaucoma Challenge (REFUGE18). We have achieved top-tier performance in all three challenges.Comment: IEEE Transactions on Medical Imagin

Classification of Radiology Reports Using Neural Attention Models

The electronic health record (EHR) contains a large amount of multi-dimensional and unstructured clinical data of significant operational and research value. Distinguished from previous studies, our approach embraces a double-annotated dataset and strays away from obscure "black-box" models to comprehensive deep learning models. In this paper, we present a novel neural attention mechanism that not only classifies clinically important findings. Specifically, convolutional neural networks (CNN) with attention analysis are used to classify radiology head computed tomography reports based on five categories that radiologists would account for in assessing acute and communicable findings in daily practice. The experiments show that our CNN attention models outperform non-neural models, especially when trained on a larger dataset. Our attention analysis demonstrates the intuition behind the classifier's decision by generating a heatmap that highlights attended terms used by the CNN model; this is valuable when potential downstream medical decisions are to be performed by human experts or the classifier information is to be used in cohort construction such as for epidemiological studies