
Breast Cancer Research, Journal Year: 2025, Volume and Issue: 27(1)
Published: March 28, 2025
Language: Английский
Breast Cancer Research, Journal Year: 2025, Volume and Issue: 27(1)
Published: March 28, 2025
Language: Английский
Nature, Journal Year: 2025, Volume and Issue: unknown
Published: Jan. 8, 2025
Language: Английский
Citations
9Medical Image Analysis, Journal Year: 2025, Volume and Issue: 101, P. 103456 - 103456
Published: Jan. 14, 2025
Language: Английский
Citations
3Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)
Published: Feb. 1, 2025
We evaluated several foundation models in histopathology for image retrieval using a zero-shot approach. These generated embeddings that were directly employed without additional fine-tuning. Our experiments conducted on diagnostic slides from The Cancer Genome Atlas (TCGA), which covers 23 organs and 117 cancer subtypes. used Yottixel as the framework whole-slide (WSI) via patch-based embeddings. Retrieval performance was macro-averaged F1 scores top-1, top-3, top-5 retrievals. indicated varying levels of performance: Yottixel-DenseNet (27% ± 13%), Yottixel-UNI (42% 14%), Yottixel-Virchow (40% Yottixel-GigaPath (41% GigaPath WSI 14%). results demonstrate potential limitations retrieval, underscoring need further advancements embedding techniques.
Language: Английский
Citations
2The Innovation Medicine, Journal Year: 2025, Volume and Issue: unknown, P. 100120 - 100120
Published: Jan. 1, 2025
<p>Artificial intelligence (AI) is driving transformative changes in the field of medicine, with its successful application relying on accurate data and rigorous quality standards. By integrating clinical information, pathology, medical imaging, physiological signals, omics data, AI significantly enhances precision research into disease mechanisms patient prognoses. technologies also demonstrate exceptional potential drug development, surgical automation, brain-computer interface (BCI) research. Through simulation biological systems prediction intervention outcomes, enables researchers to rapidly translate innovations practical applications. While challenges such as computational demands, software ethical considerations persist, future remains highly promising. plays a pivotal role addressing societal issues like low birth rates aging populations. can contribute mitigating rate through enhanced ovarian reserve evaluation, menopause forecasting, optimization Assisted Reproductive Technologies (ART), sperm analysis selection, endometrial receptivity fertility remote consultations. In posed by an population, facilitate development dementia models, cognitive health monitoring strategies, early screening systems, AI-driven telemedicine platforms, intelligent smart companion robots, environments for aging-in-place. profoundly shapes medicine.</p>
Language: Английский
Citations
2Nature Machine Intelligence, Journal Year: 2025, Volume and Issue: unknown
Published: Jan. 13, 2025
Language: Английский
Citations
2npj Breast Cancer, Journal Year: 2025, Volume and Issue: 11(1)
Published: Jan. 24, 2025
Special histologic subtypes of breast cancer (BC) exhibit unique phenotypes and molecular profiles with diagnostic therapeutic implications, often differing in behavior clinical trajectory from common BC forms. Novel methodologies, such as artificial intelligence may improve classification. Genetic predisposition plays roles a subset cases. Uncommon presentations like male, inflammatory pregnancy-related pose challenges. Emerging strategies targeting genetic alterations or immune microenvironment are being explored.
Language: Английский
Citations
1Journal of Pathology Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 100421 - 100421
Published: Jan. 1, 2025
Language: Английский
Citations
1npj Precision Oncology, Journal Year: 2025, Volume and Issue: 9(1)
Published: Jan. 30, 2025
Language: Английский
Citations
1Nature Reviews Clinical Oncology, Journal Year: 2025, Volume and Issue: unknown
Published: Feb. 11, 2025
Language: Английский
Citations
1Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)
Published: March 1, 2025
Abstract The foundation model, trained on extensive and diverse datasets, has shown strong performance across numerous downstream tasks. Nevertheless, its application in the medical domain is significantly hindered by issues such as data volume, heterogeneity, privacy concerns. Therefore, we propose Vision Foundation Model General Lightweight (VFMGL) framework, which facilitates decentralized construction of expert clinical models for various VFMGL framework transfers general knowledge from large-parameter vision to construct lightweight, robust tailored specific Through experiments analyses a range tasks scenarios, demonstrate that achieves superior both image classification segmentation tasks, effectively managing challenges posed heterogeneity. These results underscore potential advancing efficacy reliability AI-driven diagnostics.
Language: Английский
Citations
1