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pro vyhledávání: '"Gupta, Ravi. P."'
Gene selection plays a pivotal role in oncology research for improving outcome prediction accuracy and facilitating cost-effective genomic profiling for cancer patients. This paper introduces two gene selection strategies for deep learning-based surv
Externí odkaz:
http://arxiv.org/abs/2403.01927
Autor:
Parulekar, Amruta, Kanwat, Utkarsh, Gupta, Ravi Kant, Chippa, Medha, Jacob, Thomas, Bameta, Tripti, Rane, Swapnil, Sethi, Amit
Segmentation and classification of cell nuclei in histopathology images using deep neural networks (DNNs) can save pathologists' time for diagnosing various diseases, including cancers, by automating cell counting and morphometric assessments. It is
Externí odkaz:
http://arxiv.org/abs/2310.03346
This paper presents a novel approach for unsupervised domain adaptation (UDA) targeting H&E stained histology images. Existing adversarial domain adaptation methods may not effectively align different domains of multimodal distributions associated wi
Externí odkaz:
http://arxiv.org/abs/2309.17172
Autor:
Sandhan, Jivnesh, Barbadikar, Amruta, Maity, Malay, Satuluri, Pavankumar, Sandhan, Tushar, Gupta, Ravi M., Goyal, Pawan, Behera, Laxmidhar
Sanskrit poetry has played a significant role in shaping the literary and cultural landscape of the Indian subcontinent for centuries. However, not much attention has been devoted to uncovering the hidden beauty of Sanskrit poetry in computational li
Externí odkaz:
http://arxiv.org/abs/2308.07081
The heterogeneity of breast cancer presents considerable challenges for its early detection, prognosis, and treatment selection. Convolutional neural networks often neglect the spatial relationships within histopathological images, which can limit th
Externí odkaz:
http://arxiv.org/abs/2307.08132
We propose a new technique called CHATTY: Coupled Holistic Adversarial Transport Terms with Yield for Unsupervised Domain Adaptation. Adversarial training is commonly used for learning domain-invariant representations by reversing the gradients from
Externí odkaz:
http://arxiv.org/abs/2304.09623
The standard diagnostic procedures for targeted therapies in lung cancer treatment involve histological subtyping and subsequent detection of key driver mutations, such as EGFR. Even though molecular profiling can uncover the driver mutation, the pro
Externí odkaz:
http://arxiv.org/abs/2208.12506
Autor:
Hayden, Brian, Rubin, David, Boone, Kyle, Aldering, Greg, Nordin, Jakob, Brodwin, Mark, Deustua, Susana, Dixon, Sam, Fagrelius, Parker, Fruchter, Andy, Eisenhardt, Peter, Gonzalez, Anthony, Gupta, Ravi, Hook, Isobel, Lidman, Chris, Luther, Kyle, Muzzin, Adam, Raha, Zachary, Ruiz-Lapuente, Pilar, Saunders, Clare, Sofiatti, Caroline, Stanford, Adam, Suzuki, Nao, Webb, Tracy, Williams, Steven C., Wilson, Gillian, Yen, Mike, Amanullah, Rahman, Barbary, Kyle, Bohringer, Hans, Chappell, Greta, Cunha, Carlos, Currie, Miles, Fassbender, Rene, Gladders, Michael, Goobar, Ariel, Hildenrandt, Hendrik, Hoekstra, Henk, Huang, Xiaosheng, Huterer, Dragan, Jee, M. James, Kim, Alex, Kowalski, Marek, Linder, Eric, Meyers, Joshua E., Pain, Reynald, Perlmutter, Saul, Richard, Johan, Rosati, Piero, Rozo, Eduardo, Rykoff, Eli, Santos, Joana, Spadafora, Anthony, Stern, Daniel, Wechsler, Risa, Project, The Supernova Cosmology
The See Change survey was designed to make $z>1$ cosmological measurements by efficiently discovering high-redshift Type Ia supernovae (SNe Ia) and improving cluster mass measurements through weak lensing. This survey observed twelve galaxy clusters
Externí odkaz:
http://arxiv.org/abs/2103.13285
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