Accurate segmentation in histopathology images at pixel-level plays a critical role in the
digital pathology workflow. The development of weakly supervised methods for …

The problem of class imbalance has always been considered as a significant challenge to
traditional machine learning and the emerging deep learning research communities. A …

Hispathological image segmentation algorithms play a critical role in computer aided
diagnosis technology. The development of weakly supervised segmentation algorithm …

This work presents a framework based on convolutional neural networks (CNNs) to estimate
root traits (length, diameter, and color) from minirhizotron (MR) imagery. The proposed …

The total boll count from a plant is one of the most important phenotypic traits for cotton
breeding and is also an important factor for growers to estimate the final yield. With the …

Understanding a plant's root system architecture (RSA) is crucial for a variety of plant
science problem domains including sustainability and climate adaptation. Minirhizotron …

Plant roots are difficult to monitor and study since they are hidden belowground.
Minirhizotrons offer an in-situ monitoring solution but their widespread adoption is still …

Roots optimize the acquisition of limited soil resources, but relationships between root forms
and functions have often been assumed rather than demonstrated. Furthermore, how root …

Semantic segmentation networks are prone to oversegmentation in areas where objects are
tightly clustered. In minirhizotron images with densely packed plant root systems this can …

Purpose Root system architectures are complex and challenging to characterize effectively
for agronomic and ecological discovery. Methods We propose a new method, Spatial and …