Rethinking semi-supervised medical image segmentation: A variance-reduction perspective

C You, W Dai, Y Min, F Liu, D Clifton… - Advances in neural …, 2024 - proceedings.neurips.cc
For medical image segmentation, contrastive learning is the dominant practice to improve
the quality of visual representations by contrasting semantically similar and dissimilar pairs …

Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology

OL Saldanha, CML Loeffler, JM Niehues… - NPJ Precision …, 2023 - nature.com
The histopathological phenotype of tumors reflects the underlying genetic makeup. Deep
learning can predict genetic alterations from pathology slides, but it is unclear how well …

[HTML][HTML] Gram negative bacteria

J Oliveira, WC Reygaert - 2019 - europepmc.org
Gram Negative Bacteria - Abstract - Europe PMC Sign in | Create an account https://orcid.org
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Clipath: Fine-tune clip with visual feature fusion for pathology image analysis towards minimizing data collection efforts

Z Lai, Z Li, LC Oliveira, J Chauhan… - Proceedings of the …, 2023 - openaccess.thecvf.com
Abstract Contrastive Language-Image Pre-training (CLIP) has shown its ability to learn
distinctive visual representations and generalize to various downstream vision tasks …

Joint semi-supervised and active learning for segmentation of gigapixel pathology images with cost-effective labeling

Z Lai, C Wang, LC Oliveira… - Proceedings of the …, 2021 - openaccess.thecvf.com
The need for manual and detailed annotations limits the applicability of supervised deep
learning algorithms in medical image analyses, specifically in the field of pathology. Semi …

Artificial intelligence-derived neurofibrillary tangle burden is associated with antemortem cognitive impairment

GA Marx, DG Koenigsberg, AT McKenzie… - Acta Neuropathologica …, 2022 - Springer
Tauopathies are a category of neurodegenerative diseases characterized by the presence
of abnormal tau protein-containing neurofibrillary tangles (NFTs). NFTs are universally …

Implicit anatomical rendering for medical image segmentation with stochastic experts

C You, W Dai, Y Min, L Staib, JS Duncan - International Conference on …, 2023 - Springer
Integrating high-level semantically correlated contents and low-level anatomical features is
of central importance in medical image segmentation. Towards this end, recent deep …

Computational Evaluation of the Combination of Semi-Supervised and Active Learning for Histopathology Image Segmentation with Missing Annotations

LG Jiménez, L Dierckx, M Amodei… - Proceedings of the …, 2023 - openaccess.thecvf.com
Real-world segmentation tasks in digital pathology require a great effort from human experts
to accurately annotate a sufficiently high number of images. Hence, there is a huge interest …

The status of digital pathology and associated infrastructure within Alzheimer's Disease Centers

R Scalco, Y Hamsafar, CL White III… - … of Neuropathology & …, 2023 - academic.oup.com
Digital pathology (DP) has transformative potential, especially for Alzheimer disease and
related disorders. However, infrastructure barriers may limit adoption. To provide …

How cy pres promotes transdisciplinary convergence science: an academic health center for women's cardiovascular and brain health

A Villablanca, BN Dugger, S Nuthikattu… - Journal of Clinical and …, 2024 - cambridge.org
Cardiovascular disease (CVD) is largely preventable, and the leading cause of death for
men and women. Though women have increased life expectancy compared to men, there …