Crop production can be greatly reduced due to various diseases, which seriously endangers
food security. Thus, detecting plant diseases accurately is necessary and urgent. Traditional …

The advent of big data and algorithmic developments in the field of machine learning (and
artificial intelligence, in general) have greatly impacted the entire spectrum of physical …

Abstract Computer-Aided Diagnosis (CAD) for dermatological diseases offers one of the
most notable showcases where deep learning technologies display their impressive …

The U-shaped network is one of the end-to-end convolutional neural networks (CNNs). In
electron microscope segmentation of ISBI challenge 2012, the concise architecture and …

For the past decade, convolutional networks have been used for 3D reconstruction of
neurons from electron microscopic (EM) brain images. Recent years have seen great …

Cellular-resolution connectomics is an ambitious research direction with the goal of
generating comprehensive brain connectivity maps using high-throughput, nano-scale …

In this paper, we introduce a conceptually simple network for generating discriminative
tissue-level segmentation masks for the purpose of breast cancer diagnosis. Our method …

Deep learning has shown its great promise in various biomedical image segmentation tasks.
Existing models are typically based on U-Net and rely on an encoder-decoder architecture …

In this paper, we introduce a simple, yet powerful pipeline for medical image segmentation
that combines Fully Convolutional Networks (FCNs) with Fully Convolutional Residual …

Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …