Titlebook: Applications of Medical Artificial Intelligence; First International Shandong Wu,Behrouz Shabestari,Lei Xing Conference proceedings 2022 T

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,Deep Neural Network Pruning for Nuclei Instance Segmentation in Hematoxylin and Eosin-Stained Histoand increasing inference speed on specialized hardwares. Although pruning was mainly tested on computer vision tasks, its application in the context of medical image analysis has hardly been explored. This work investigates the impact of well-known pruning techniques, namely layer-wise and network-w

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Spatial Feature Conservation Networks (SFCNs) for Dilated Convolutions to Improve Breast Cancer Segreatment planning. Deep learning has tremendously improved the performances of automated segmentation in a data-driven manner as compared with conventional machine learning models. In this work, we propose a spatial feature conservative design for feature extraction in deep neural networks. To avoid

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,The Impact of Using Voxel-Level Segmentation Metrics on Evaluating Multifocal Prostate Cancer Locald, when reported alone, for their unclear or even misleading clinical interpretation. DSCs may also differ substantially from HDs, due to boundary smoothness or multiple regions of interest (ROIs) within a subject. More importantly, either metric can also have a nonlinear, non-monotonic relationship

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,OOOE: Only-One-Object-Exists Assumption to Find Very Small Objects in Chest Radiographs, neural networks could potentially automate. However, many foreign objects like tubes and various anatomical structures are small in comparison to the entire chest X-ray, which leads to severely unbalanced data and makes training deep neural networks difficult. In this paper, we present a simple yet

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,Wavelet Guided 3D Deep Model to Improve Dental Microfracture Detection, crack will continue to progress, often with significant pain, until the tooth is lost. Previous attempts to utilize cone beam computed tomography (CBCT) for detecting cracks in teeth had very limited success. We propose a model that detects cracked teeth in high resolution (hr) CBCT scans by combin

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,Analysis of Potential Biases on Mammography Datasets for Deep Learning Model Development, levels. Furthermore, we summarize some techniques to alleviate these biases for the development of fair deep learning models. We present a learning task to classify negative and positive screening mammographies and analyze the influence of biases in the performance of the algorithm.

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