Zobrazeno 1 - 10
of 891
pro vyhledávání: '"MAHMOOD, Faisal"'
Autor:
Jaume, Guillaume, Vaidya, Anurag, Zhang, Andrew, Song, Andrew H., Chen, Richard J., Sahai, Sharifa, Mo, Dandan, Madrigal, Emilio, Le, Long Phi, Mahmood, Faisal
Developing self-supervised learning (SSL) models that can learn universal and transferable representations of H&E gigapixel whole-slide images (WSIs) is becoming increasingly valuable in computational pathology. These models hold the potential to adv
Externí odkaz:
http://arxiv.org/abs/2408.02859
Autor:
Kolbeinsson, Arinbjorn, O'Brien, Kyle, Huang, Tianjin, Gao, Shanghua, Liu, Shiwei, Schwarz, Jonathan Richard, Vaidya, Anurag, Mahmood, Faisal, Zitnik, Marinka, Chen, Tianlong, Hartvigsen, Thomas
Test-time interventions for language models can enhance factual accuracy, mitigate harmful outputs, and improve model efficiency without costly retraining. But despite a flood of new methods, different types of interventions are largely developing in
Externí odkaz:
http://arxiv.org/abs/2407.06483
Autor:
Song, Andrew H., Chen, Richard J., Jaume, Guillaume, Vaidya, Anurag J., Baras, Alexander S., Mahmood, Faisal
Multimodal survival methods combining gigapixel histology whole-slide images (WSIs) and transcriptomic profiles are particularly promising for patient prognostication and stratification. Current approaches involve tokenizing the WSIs into smaller pat
Externí odkaz:
http://arxiv.org/abs/2407.00224
Autor:
Jaume, Guillaume, Doucet, Paul, Song, Andrew H., Lu, Ming Y., Almagro-Pérez, Cristina, Wagner, Sophia J., Vaidya, Anurag J., Chen, Richard J., Williamson, Drew F. K., Kim, Ahrong, Mahmood, Faisal
Spatial transcriptomics enables interrogating the molecular composition of tissue with ever-increasing resolution and sensitivity. However, costs, rapidly evolving technology, and lack of standards have constrained computational methods in ST to narr
Externí odkaz:
http://arxiv.org/abs/2406.16192
Autor:
Gao, Gan, Song, Andrew H., Wang, Fiona, Brenes, David, Wang, Rui, Chow, Sarah S. L., Bishop, Kevin W., True, Lawrence D., Mahmood, Faisal, Liu, Jonathan T. C.
Publikováno v:
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 6955-6965
Accurate patient diagnoses based on human tissue biopsies are hindered by current clinical practice, where pathologists assess only a limited number of thin 2D tissue slices sectioned from 3D volumetric tissue. Recent advances in non-destructive 3D p
Externí odkaz:
http://arxiv.org/abs/2406.07061
Autor:
Song, Andrew H., Chen, Richard J., Ding, Tong, Williamson, Drew F. K., Jaume, Guillaume, Mahmood, Faisal
Representation learning of pathology whole-slide images (WSIs) has been has primarily relied on weak supervision with Multiple Instance Learning (MIL). However, the slide representations resulting from this approach are highly tailored to specific cl
Externí odkaz:
http://arxiv.org/abs/2405.11643
Autor:
Jaume, Guillaume, Oldenburg, Lukas, Vaidya, Anurag, Chen, Richard J., Williamson, Drew F. K., Peeters, Thomas, Song, Andrew H., Mahmood, Faisal
Self-supervised learning (SSL) has been successful in building patch embeddings of small histology images (e.g., 224x224 pixels), but scaling these models to learn slide embeddings from the entirety of giga-pixel whole-slide images (WSIs) remains cha
Externí odkaz:
http://arxiv.org/abs/2405.11618
Autor:
Song, Andrew H., Jaume, Guillaume, Williamson, Drew F. K., Lu, Ming Y., Vaidya, Anurag, Miller, Tiffany R., Mahmood, Faisal
Publikováno v:
Nature Reviews Bioengineering 2023
Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence, including deep learning, have boosted the field of computational pathology. This field holds tremendous potential to automate clinical diagnosis, predict pat
Externí odkaz:
http://arxiv.org/abs/2401.06148
Autor:
Lu, Ming Y., Chen, Bowen, Williamson, Drew F. K., Chen, Richard J., Ikamura, Kenji, Gerber, Georg, Liang, Ivy, Le, Long Phi, Ding, Tong, Parwani, Anil V, Mahmood, Faisal
The field of computational pathology has witnessed remarkable progress in the development of both task-specific predictive models and task-agnostic self-supervised vision encoders. However, despite the explosive growth of generative artificial intell
Externí odkaz:
http://arxiv.org/abs/2312.07814
Autor:
Chen, Richard J., Ding, Tong, Lu, Ming Y., Williamson, Drew F. K., Jaume, Guillaume, Chen, Bowen, Zhang, Andrew, Shao, Daniel, Song, Andrew H., Shaban, Muhammad, Williams, Mane, Vaidya, Anurag, Sahai, Sharifa, Oldenburg, Lukas, Weishaupt, Luca L., Wang, Judy J., Williams, Walt, Le, Long Phi, Gerber, Georg, Mahmood, Faisal
Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology. However, whole-slide imaging (WSI) poses a complex computer vision problem in which
Externí odkaz:
http://arxiv.org/abs/2308.15474