Zobrazeno 1 - 10
of 77
pro vyhledávání: '"Kaczmarzyk, Jakub"'
Deep learning models have shown promise in histopathology image analysis, but their opaque decision-making process poses challenges in high-risk medical scenarios. Here we introduce HIPPO, an explainable AI method that interrogates attention-based mu
Externí odkaz:
http://arxiv.org/abs/2409.03080
Autor:
Kaczmarzyk, Jakub R., O'Callaghan, Alan, Inglis, Fiona, Kurc, Tahsin, Gupta, Rajarsi, Bremer, Erich, Bankhead, Peter, Saltz, Joel H.
The field of digital pathology has seen a proliferation of deep learning models in recent years. Despite substantial progress, it remains rare for other researchers and pathologists to be able to access models published in the literature and apply th
Externí odkaz:
http://arxiv.org/abs/2309.04631
The combination of data analysis methods, increasing computing capacity, and improved sensors enable quantitative granular, multi-scale, cell-based analyses. We describe the rich set of application challenges related to tissue interpretation and surv
Externí odkaz:
http://arxiv.org/abs/2206.07573
Autor:
Kaczmarzyk, Jakub R., Kurc, Tahsin M., Abousamra, Shahira, Gupta, Rajarsi, Saltz, Joel H., Koo, Peter K.
Histopathology remains the gold standard for diagnosis of various cancers. Recent advances in computer vision, specifically deep learning, have facilitated the analysis of histopathology images for various tasks, including immune cell detection and m
Externí odkaz:
http://arxiv.org/abs/2206.06862
Autor:
Hasan, Mahmudul, Kaczmarzyk, Jakub R., Paredes, David, Oblein, Lyanne, Oentoro, Jaymie, Abousamra, Shahira, Horowitz, Michael, Samaras, Dimitris, Chen, Chao, Kurc, Tahsin, Shroyer, Kenneth R., Saltz, Joel
Understanding the impact of tumor biology on the composition of nearby cells often requires characterizing the impact of biologically distinct tumor regions. Biomarkers have been developed to label biologically distinct tumor regions, but challenges
Externí odkaz:
http://arxiv.org/abs/2204.12283
Autor:
Kaczmarzyk, Jakub R., Gupta, Rajarsi, Kurc, Tahsin M., Abousamra, Shahira, Saltz, Joel H., Koo, Peter K.
Publikováno v:
In Computer Methods and Programs in Biomedicine September 2023 239
Autor:
McClure, Patrick, Rho, Nao, Lee, John A., Kaczmarzyk, Jakub R., Zheng, Charles, Ghosh, Satrajit S., Nielson, Dylan, Thomas, Adam G., Bandettini, Peter, Pereira, Francisco
In this paper, we describe a Bayesian deep neural network (DNN) for predicting FreeSurfer segmentations of structural MRI volumes, in minutes rather than hours. The network was trained and evaluated on a large dataset (n = 11,480), obtained by combin
Externí odkaz:
http://arxiv.org/abs/1812.01719
Autor:
McClure, Patrick, Zheng, Charles Y., Kaczmarzyk, Jakub R., Lee, John A., Ghosh, Satrajit S., Nielson, Dylan, Bandettini, Peter, Pereira, Francisco
Collecting the large datasets needed to train deep neural networks can be very difficult, particularly for the many applications for which sharing and pooling data is complicated by practical, ethical, or legal concerns. However, it may be the case t
Externí odkaz:
http://arxiv.org/abs/1805.10863
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