Despite the remarkable performance of deep learning methods on various tasks, most
cutting-edge models rely heavily on large-scale annotated training examples, which are …

Deep learning has an enormous impact on medical image analysis. Many computer-aided
diagnostic systems equipped with deep networks are rapidly reducing human intervention in …

Electron microscopy (EM) enables high-resolution imaging of tissues and cells based on 2D
and 3D imaging techniques. Due to the laborious and time-consuming nature of manual …

Existing methods for unsupervised domain adaptive mitochondria segmentation perform
feature alignment via adversarial learning, and achieve promising performance. However …

Abstract Background and Objective: Accurate segmentation of electron microscopy (EM)
volumes of the brain is essential to characterize neuronal structures at a cell or organelle …

The method proposed in this paper is a robust combination of multi-task learning and
unsupervised domain adaptation for segmenting amoeboid cells in microscopy. A highlight …

A deep learning (DL) method for accelerated magnetic resonance (MR) imaging is
presented that incorporates domain knowledge of parallel MR imaging to augment the DL …

Deep learning-based methods for mitochondria segmentation require sufficient annotations
on Electron Microscopy (EM) volumes, which are often expensive and time-consuming to …

Abstract Background and Objectives: Cryo electron tomography visualizes native cells at
nanometer resolution, but analysis is challenged by noise and artifacts. Recently …

Lesion segmentation of ultrasound medical images based on deep learning techniques is a
widely used method for diagnosing diseases. Although there is a large amount of ultrasound …