Siddhi Ramesh
Overview
Explore the profile of Siddhi Ramesh including associated specialties, affiliations and a list of published articles.
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Articles
15
Citations
184
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0
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Recent Articles
1.
Howard F, Hieromnimon H, Ramesh S, Dolezal J, Kochanny S, Zhang Q, et al.
Sci Adv
. 2024 Nov;
10(46):eadq0856.
PMID: 39546597
Artificial intelligence models have been increasingly used in the analysis of tumor histology to perform tasks ranging from routine classification to identification of molecular features. These approaches distill cancer histologic...
2.
Ramesh S, Dyer E, Pomaville M, Doytcheva K, Dolezal J, Kochanny S, et al.
NPJ Precis Oncol
. 2024 Nov;
8(1):255.
PMID: 39511421
A deep learning model using attention-based multiple instance learning (aMIL) and self-supervised learning (SSL) was developed to perform pathologic classification of neuroblastic tumors and assess MYCN-amplification status using H&E-stained whole...
3.
Choudhury D, Dolezal J, Dyer E, Kochanny S, Ramesh S, Howard F, et al.
EBioMedicine
. 2024 Aug;
107:105276.
PMID: 39197222
Background: Deployment and access to state-of-the-art precision medicine technologies remains a fundamental challenge in providing equitable global cancer care in low-resource settings. The expansion of digital pathology in recent years...
4.
Applebaum M, Ramesh S, Dyer E, Pomaville M, Doytcheva K, Dolezal J, et al.
Res Sq
. 2024 Jun;
PMID: 38883758
A deep learning model using attention-based multiple instance learning (aMIL) and self-supervised learning (SSL) was developed to perform pathologic classification of neuroblastic tumors and assess -amplification status using H&E-stained whole...
5.
Ramesh S, Tomesh T, Riesenfeld S, Chong F, Pearson A
Nat Cancer
. 2024 May;
5(6):811-816.
PMID: 38760645
No abstract available.
6.
Howard F, Hieromnimon H, Ramesh S, Dolezal J, Kochanny S, Zhang Q, et al.
bioRxiv
. 2024 Apr;
PMID: 38585926
Artificial intelligence models have been increasingly used in the analysis of tumor histology to perform tasks ranging from routine classification to identification of novel molecular features. These approaches distill cancer...
7.
Dolezal J, Kochanny S, Dyer E, Ramesh S, Srisuwananukorn A, Sacco M, et al.
BMC Bioinformatics
. 2024 Mar;
25(1):134.
PMID: 38539070
Deep learning methods have emerged as powerful tools for analyzing histopathological images, but current methods are often specialized for specific domains and software environments, and few open-source options exist for...
8.
Dolezal J, Wolk R, Hieromnimon H, Howard F, Srisuwananukorn A, Karpeyev D, et al.
NPJ Precis Oncol
. 2023 May;
7(1):49.
PMID: 37248379
Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists. Discerning how neural networks...
9.
Gao C, Howard F, Markov N, Dyer E, Ramesh S, Luo Y, et al.
NPJ Digit Med
. 2023 Apr;
6(1):75.
PMID: 37100871
Large language models such as ChatGPT can produce increasingly realistic text, with unknown information on the accuracy and integrity of using these models in scientific writing. We gathered fifth research...
10.
Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology
Saldanha O, Loeffler C, Niehues J, van Treeck M, Seraphin T, Hewitt K, et al.
NPJ Precis Oncol
. 2023 Mar;
7(1):35.
PMID: 36977919
The histopathological phenotype of tumors reflects the underlying genetic makeup. Deep learning can predict genetic alterations from pathology slides, but it is unclear how well these predictions generalize to external...