Corrigendum to "Decoding phenotypic screening: A comparative analysis of image representations" [Computat Struct Biotechnol J 23 (2024) 1181-1188].

[This corrects the article DOI: 10.1016/j.csbj.

Decoding phenotypic screening: A comparative analysis of image representations.

Biomedical imaging techniques such as high content screening (HCS) are valuable for drug discovery, but high costs limit their use to pharmaceutical companies. To address this issue, The JUMP-CP consortium released a massive open image dataset of chemical and genetic perturbations, providing a valuable resource for deep learning research. In this work, we aim to utilize the JUMP-CP dataset to develop a universal representation model for HCS data, mainly data generated using U2OS cells and CellPainting protocol, using supervised and self-supervised learning approaches.

Deep Learning Models Capture Histological Disease Activity in Crohn's Disease and Ulcerative Colitis with High Fidelity.

Background And Aims: Histological disease activity in inflammatory bowel disease [IBD] is associated with clinical outcomes and is an important endpoint in drug development. We developed deep learning models for automating histological assessments in IBD.

Methods: Histology images of intestinal mucosa from phase 2 and phase 3 clinical trials in Crohn's disease [CD] and ulcerative colitis [UC] were used to train artificial intelligence [AI] models to predict the Global Histology Activity Score [GHAS] for CD and Geboes histopathology score for UC.

Identifying Bacteria Species on Microscopic Polyculture Images Using Deep Learning.

Preliminary microbiological diagnosis usually relies on microscopic examination and, due to the routine culture and bacteriological examination, lasts up to 11 days. Hence, many deep learning methods based on microscopic images were recently introduced to replace the time-consuming bacteriological examination. They shorten the diagnosis by 1-2 days but still require iterative culture to obtain monoculture samples.

Comparison of Supervised and Self-Supervised Deep Representations Trained on Histological Images.

Self-supervised methods gain more and more attention, especially in the medical domain, where the number of labeled data is limited. They provide results on par or superior to their fully supervised competitors, yet the difference between information coded by both methods is unclear. This work introduces a novel comparison framework for explaining differences between supervised and self-supervised models using visual characteristics important to the human perceptual system.