Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Kurç, Tahsin M."'
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:
Baid, Ujjwal, Pati, Sarthak, Kurc, Tahsin M., Gupta, Rajarsi, Bremer, Erich, Abousamra, Shahira, Thakur, Siddhesh P., Saltz, Joel H., Bakas, Spyridon
We evaluate the performance of federated learning (FL) in developing deep learning models for analysis of digitized tissue sections. A classification application was considered as the example use case, on quantifiying the distribution of tumor infilt
Externí odkaz:
http://arxiv.org/abs/2203.16622
Autor:
Pati, Sarthak, Thakur, Siddhesh P., Hamamcı, İbrahim Ethem, Baid, Ujjwal, Baheti, Bhakti, Bhalerao, Megh, Güley, Orhun, Mouchtaris, Sofia, Lang, David, Thermos, Spyridon, Gotkowski, Karol, González, Camila, Grenko, Caleb, Getka, Alexander, Edwards, Brandon, Sheller, Micah, Wu, Junwen, Karkada, Deepthi, Panchumarthy, Ravi, Ahluwalia, Vinayak, Zou, Chunrui, Bashyam, Vishnu, Li, Yuemeng, Haghighi, Babak, Chitalia, Rhea, Abousamra, Shahira, Kurc, Tahsin M., Gastounioti, Aimilia, Er, Sezgin, Bergman, Mark, Saltz, Joel H., Fan, Yong, Shah, Prashant, Mukhopadhyay, Anirban, Tsaftaris, Sotirios A., Menze, Bjoern, Davatzikos, Christos, Kontos, Despina, Karargyris, Alexandros, Umeton, Renato, Mattson, Peter, Bakas, Spyridon
Publikováno v:
Commun Eng 2, 23 (2023)
Deep Learning (DL) has the potential to optimize machine learning in both the scientific and clinical communities. However, greater expertise is required to develop DL algorithms, and the variability of implementations hinders their reproducibility,
Externí odkaz:
http://arxiv.org/abs/2103.01006
Autor:
Hou, Le, Gupta, Rajarsi, Van Arnam, John S., Zhang, Yuwei, Sivalenka, Kaustubh, Samaras, Dimitris, Kurc, Tahsin M., Saltz, Joel H.
Publikováno v:
Sci Data 7, 185 (2020)
The distribution and appearance of nuclei are essential markers for the diagnosis and study of cancer. Despite the importance of nuclear morphology, there is a lack of large scale, accurate, publicly accessible nucleus segmentation data. To address t
Externí odkaz:
http://arxiv.org/abs/2002.07913
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:
Hou, Le, Agarwal, Ayush, Samaras, Dimitris, Kurc, Tahsin M., Gupta, Rajarsi R., Saltz, Joel H.
Hematoxylin and Eosin stained histopathology image analysis is essential for the diagnosis and study of complicated diseases such as cancer. Existing state-of-the-art approaches demand extensive amount of supervised training data from trained patholo
Externí odkaz:
http://arxiv.org/abs/1712.05021
Autor:
Hou, Le, Nguyen, Vu, Samaras, Dimitris, Kurc, Tahsin M., Gao, Yi, Zhao, Tianhao, Saltz, Joel H.
Histopathology images are crucial to the study of complex diseases such as cancer. The histologic characteristics of nuclei play a key role in disease diagnosis, prognosis and analysis. In this work, we propose a sparse Convolutional Autoencoder (CAE
Externí odkaz:
http://arxiv.org/abs/1704.00406
Classifying the various shapes and attributes of a glioma cell nucleus is crucial for diagnosis and understanding the disease. We investigate automated classification of glioma nuclear shapes and visual attributes using Convolutional Neural Networks
Externí odkaz:
http://arxiv.org/abs/1612.06825
In Neural Networks (NN), Adaptive Activation Functions (AAF) have parameters that control the shapes of activation functions. These parameters are trained along with other parameters in the NN. AAFs have improved performance of Neural Networks (NN) i
Externí odkaz:
http://arxiv.org/abs/1608.06557
Convolutional Neural Networks (CNN) are state-of-the-art models for many image classification tasks. However, to recognize cancer subtypes automatically, training a CNN on gigapixel resolution Whole Slide Tissue Images (WSI) is currently computationa
Externí odkaz:
http://arxiv.org/abs/1504.07947