17 citations · 39 across the 7 of their papers we have counts for
7 papers
CLANet: A Comprehensive Framework for Cross-Batch Cell Line Identification Using Brightfield Images
Lei Tong, Adam Corrigan, Navin Rathna Kumar +4
Cell line authentication plays a crucial role in the biomedical field, ensuring researchers work with accurately identified cells. Supervised deep learning has made remarkable stri…
Class-Guided Image-to-Image Diffusion: Cell Painting from Brightfield Images with Class Labels
Jan Oscar Cross-Zamirski, Praveen Anand, Guy Williams +3
Image-to-image reconstruction problems with free or inexpensive metadata in the form of class labels appear often in biological and medical image domains. Existing text-guided or s…
Self-Supervised Learning of Phenotypic Representations from Cell Images with Weak Labels
Jan Oscar Cross-Zamirski, Guy Williams, Elizabeth Mouchet +3
We propose WS-DINO as a novel framework to use weak label information in learning phenotypic representations from high-content fluorescent images of cells. Our model is based on a…
Deep Learning Based Brain Tumor Segmentation: A Survey
Zhihua Liu, Lei Tong, Zheheng Jiang +6
Brain tumor segmentation is one of the most challenging problems in medical image analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain tumor…
CANet: Context Aware Network for 3D Brain Glioma Segmentation
Zhihua Liu, Lei Tong, Long Chen +7
Automated segmentation of brain glioma plays an active role in diagnosis decision, progression monitoring and surgery planning. Based on deep neural networks, previous studies have…
Cost-sensitive Boosting Pruning Trees for depression detection on Twitter
Lei Tong, Zhihua Liu, Zheheng Jiang +9
Depression is one of the most common mental health disorders, and a large number of depressed people commit suicide each year. Potential depression sufferers usually do not consult…
Phenotypic Profiling of High Throughput Imaging Screens with Generic Deep Convolutional Features
Philip T. Jackson, Yinhai Wang, Sinead Knight +5
While deep learning has seen many recent applications to drug discovery, most have focused on predicting activity or toxicity directly from chemical structure. Phenotypic changes e…