Jane Hung

TL;DR: The steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images are introduced and techniques that have proven useful in each stage of the data analysis process are recommended on the basis of the experience of 20 laboratories worldwide that are refining their image- based cell-profiling methodologies.

TL;DR: The Plug and Play Language Model (PPLM) for controllable language generation is proposed, which combines a pretrained LM with one or more simple attribute classifiers that guide text generation without any further training of the LM.

TL;DR: The Plug and Play Language Model (PPLM) as mentioned in this paper combines a pre-trained transformer-based language model with one or more simple attribute classifiers that guide text generation without any further training of the transformer.

TL;DR: In this paper, a deep neural network model was proposed to predict an absolute measure of image focus on a single image in isolation, without any user-specified parameters, and also outputs a measure of prediction certainty, enabling interpretable predictions.

TL;DR: Faster R-CNN as mentioned in this paper was used to identify cells and recognize their stages in bright-field microscopy images of malaria-infected blood, which is the first time an object detection model has been applied to biological image data.