Griebel, M. | bioRxiv Science

bioRxiv2022

A deep learning model trained on only eight whole-slide images accurately segments tumors: wise data use versus big data

Computer-assisted pathology is one of the biggest challenges in the medicine of the future. However, artificial intelligence is struggling to gain acceptance in the broader medical community due to data security issues, lack of trust in the machine, and poor data availability. Here, we develop a tumor delineation algorithm with only eight whole slide images of ovarian cancer to demonstrate the feasibility of an artificial intelligence application created from only a few data, finely annotated and with optimal processing. We test the model on seventeen other slides from the same hospital. The predictions are similar to the ground truth annotations made by an expert pathologist, with a mean DICE score of 0.90 [0.85 - 0.93]. The results on slides from another hospital are consistent, suggesting that the model is generalizable and that its performance does not suffer from different data acquisition. This study demonstrates the feasibility of a contouring algorithm based on a reduced dataset well optimized, going against the commonly accepted idea that a phenomenal amount of data is paramount. This study paves the way for other medical applications, especially for rare pathologies with limited available data.

bioinformatics↗

bioRxiv2018

DeepFLaSh, a deep learning pipeline for segmentation of fluorescent labels in microscopy images

Fluorescent labeling of biomolecules is widely used for bioimage analyses throughout the life sciences. Recent advances in deep learning (DL) have opened new possibilities to scale the image analysis processes through automation. However, the annotation of fluorescent features with a low signal-to-noise ratio is frequently based on subjective criteria. Training on subjective annotations may ultimately lead to biased DL models yielding irreproducible results. An end-to-end analysis process that integrates data annotation, ground truth estimation, and model training can mitigate this risk. To highlight the importance of this integrated process, we compare different DL-based analysis approaches. Based on data from different laboratories, we show that ground truth estimation from multiple human annotators is indispensable to establish objectivity in fluorescent feature annotations. We demonstrate that ensembles of multiple models trained on the estimated ground truth establish reliability and validity. Our research provides guidelines for reproducible and transparent bioimage analyses using DL methods.

neuroscience↗