A systematic review on overfitting control in shallow and deep neural networks
Shallow neural networks process the features directly, while deep networks extract features
automatically along with the training. Both models suffer from overfitting or poor …
automatically along with the training. Both models suffer from overfitting or poor …
Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
Abstract Machine learning (ML) algorithms have made a tremendous impact in the field of
medical imaging. While medical imaging datasets have been growing in size, a challenge …
medical imaging. While medical imaging datasets have been growing in size, a challenge …
Towards label-efficient automatic diagnosis and analysis: a comprehensive survey of advanced deep learning-based weakly-supervised, semi-supervised and self …
Histopathological images contain abundant phenotypic information and pathological
patterns, which are the gold standards for disease diagnosis and essential for the prediction …
patterns, which are the gold standards for disease diagnosis and essential for the prediction …
Cell image segmentation using generative adversarial networks, transfer learning, and augmentations
We address the problem of segmenting cell contours from microscopy images of human
induced pluripotent Retinal Pigment Epithelial stem cells (iRPE) using Convolutional Neural …
induced pluripotent Retinal Pigment Epithelial stem cells (iRPE) using Convolutional Neural …
Generative adversarial learning for reducing manual annotation in semantic segmentation on large scale miscroscopy images: Automated vessel segmentation in …
Abstract Convolutional Neural Network (CNN) based semantic segmentation require
extensive pixel level manual annotation which is daunting for large microscopic images. The …
extensive pixel level manual annotation which is daunting for large microscopic images. The …
[HTML][HTML] Natural and Artificial Intelligence: A brief introduction to the interplay between AI and neuroscience research
Neuroscience and artificial intelligence (AI) share a long history of collaboration. Advances
in neuroscience, alongside huge leaps in computer processing power over the last few …
in neuroscience, alongside huge leaps in computer processing power over the last few …
Renal cell carcinoma detection and subtyping with minimal point-based annotation in whole-slide images
Z Gao, P Puttapirat, J Shi, C Li - … Conference, Lima, Peru, October 4–8 …, 2020 - Springer
Cancerous region detection and subtyping in whole-slide images (WSIs) are fundamental
for renal cell carcinoma (RCC) diagnosis. The main challenge in the development of …
for renal cell carcinoma (RCC) diagnosis. The main challenge in the development of …
Robust object tracking using manifold regularized convolutional neural networks
In visual tracking, usually only a small number of samples are labeled, and most existing
deep learning based trackers ignore abundant unlabeled samples that could provide …
deep learning based trackers ignore abundant unlabeled samples that could provide …
Neuronal population reconstruction from ultra-scale optical microscopy images via progressive learning
Reconstruction of neuronal populations from ultra-scale optical microscopy (OM) images is
essential to investigate neuronal circuits and brain mechanisms. The noises, low contrast …
essential to investigate neuronal circuits and brain mechanisms. The noises, low contrast …
U-Net: A valuable encoder-decoder architecture for liver tumors segmentation in CT images
This study proposes a new predictive segmentation method for liver tumors detection using
computed tomography (CT) liver images. In the medical imaging field, the exact localization …
computed tomography (CT) liver images. In the medical imaging field, the exact localization …