Meta-transfer Learning for Brain Tumor Segmentation: Within and Beyond Glioma.

In recent years, numerous algorithms have emerged for the segmentation of brain tumors, propelled by both the advancements of deep learning techniques and the influential open benchmark set by the BraTS challenge. This chapter provides an overview of the background that gave rise to automated brain tumor segmentation algorithms, reviews representative deep learning-based approaches, and reflects their limits on clinical applicability. While these algorithms showcase promising results in fully supervised settings, they may not perform well to other types of brain tumors without substantial samples for model re-training or fine-tuning.

Multi-degradation-adaptation network for fundus image enhancement with degradation representation learning.

Fundus image quality serves a crucial asset for medical diagnosis and applications. However, such images often suffer degradation during image acquisition where multiple types of degradation can occur in each image. Although recent deep learning based methods have shown promising results in image enhancement, they tend to focus on restoring one aspect of degradation and lack generalisability to multiple modes of degradation.

An annotated grain kernel image database for visual quality inspection.

We present a machine vision-based database named GrainSet for the purpose of visual quality inspection of grain kernels. The database contains more than 350K single-kernel images with experts' annotations. The grain kernels used in the study consist of four types of cereal grains including wheat, maize, sorghum and rice, and were collected from over 20 regions in 5 countries.

SAC-Net: Learning with weak and noisy labels in histopathology image segmentation.

Deep convolutional neural networks have been highly effective in segmentation tasks. However, segmentation becomes more difficult when training images include many complex instances to segment, such as the task of nuclei segmentation in histopathology images. Weakly supervised learning can reduce the need for large-scale, high-quality ground truth annotations by involving non-expert annotators or algorithms to generate supervision information for segmentation.