Analysis of the impact of a university distance learning course on digitalization in medicine on students and healthcare professionals.

Purpose: The public medical universities in Austria (educating 11,000 students) developed a joint public distance learning series in which clinicians discussed current digital lighthouse projects in their specialty. This study aims to examine the changes in attitude and knowledge of the participants before and after the lecture series to gain insights for future curriculum developments.

Method: The lecture series was announced via various channels at the universities, in health newsletters and in social media.

NuInsSeg: A fully annotated dataset for nuclei instance segmentation in H&E-stained histological images.

In computational pathology, automatic nuclei instance segmentation plays an essential role in whole slide image analysis. While many computerized approaches have been proposed for this task, supervised deep learning (DL) methods have shown superior segmentation performances compared to classical machine learning and image processing techniques. However, these models need fully annotated datasets for training which is challenging to acquire, especially in the medical domain.

Improving generalization capability of deep learning-based nuclei instance segmentation by non-deterministic train time and deterministic test time stain normalization.

With the advent of digital pathology and microscopic systems that can scan and save whole slide histological images automatically, there is a growing trend to use computerized methods to analyze acquired images. Among different histopathological image analysis tasks, nuclei instance segmentation plays a fundamental role in a wide range of clinical and research applications. While many semi- and fully-automatic computerized methods have been proposed for nuclei instance segmentation, deep learning (DL)-based approaches have been shown to deliver the best performances.