As a typical biomedical detection task, nuclei detection has been widely used in human health management, disease diagnosis and other fields. However, the task of cell detection in microscopic images is still challenging because the nuclei are common...
PET images often suffer poor signal-to-noise ratio (SNR). Our objective is to improve the SNR of PET images using a deep neural network (DNN) model and MRI images without requiring any higher SNR PET images in training. Our proposed DNN model consist...
Automatic and accurate classification of apoptosis, or programmed cell death, will facilitate cell biology research. The state-of-the-art approaches in apoptosis classification use deep convolutional neural networks (CNNs). However, these networks ar...
IEEE transactions on biomedical circuits and systems
May 22, 2019
The paper proposes an innovative deep convolutional neural network (DCNN) combined with texture map for detecting cancerous regions and marking the ROI in a single model automatically. The proposed DCNN model contains two collaborative branches, name...
PURPOSE: Recent advances in machine learning have enabled better understanding of large and complex visual data. Here, we aim to investigate patient outcome prediction with a machine learning method using only an image of tumour sample as an input.
PURPOSE: Stereotactic radiosurgery (SRS) is widely used to obliterate arteriovenous malformations (AVMs). Its performance relies on the accuracy of delineating the target AVM. Manual segmentation during a framed SRS procedure is time consuming and su...
BACKGROUND: Medical datasets, especially medical images, are often imbalanced due to the different incidences of various diseases. To address this problem, many methods have been proposed to synthesize medical images using generative adversarial netw...
Although distinct categories are reliably decoded from fMRI brain responses, it has proved more difficult to distinguish visually similar inputs, such as different faces. Here, we apply a recently developed deep learning system to reconstruct face im...
IEEE transactions on pattern analysis and machine intelligence
May 20, 2019
Gradient descent optimization of learning has become a paradigm for training deep convolutional neural networks (DCNN). However, utilizing other learning strategies in the training process of the DCNN has rarely been explored by the deep learning (DL...
Fully convolutional networks have been achieving remarkable results in image semantic segmentation, while being efficient. Such efficiency results from the capability of segmenting several voxels in a single forward pass. So, there is a direct spatia...
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