ACMIT and its scientific partners have published a new paper in the Computational and Structural Biotechnology Journal (CSBJ) entitled “Improving generalization capability of deep learning-based nuclei instance segmentation by non-deterministic train time and deterministic test time stain normalization”.
In this work, which was a collaboration between Danube Private University, the Medical University of Vienna, and ACMIT, a novel method to improve the generalization capability of a deep learning (DL)-based nuclei segmentation is proposed. Besides utilizing one of the state-of-the-art DL-based models as a baseline, our proposed approach incorporates non-deterministic train time and deterministic test time stain normalization and ensembling to boost the segmentation performance. The model was trained with one single training set, and its segmentation performance was evaluated on seven test datasets. The results show that the proposed method provides superior performance in segmenting nuclei compared to the baseline segmentation model.
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