Search Results - "Verghese, Gregory"

Computational pathology in cancer diagnosis, prognosis, and prediction – present day and prospects by Verghese, Gregory, Lennerz, Jochen K, Ruta, Danny, Ng, Wen, Thavaraj, Selvam, Siziopikou, Kalliopi P, Naidoo, Threnesan, Rane, Swapnil, Salgado, Roberto, Pinder, Sarah E, Grigoriadis, Anita

“…Computational pathology refers to applying deep learning techniques and algorithms to analyse and interpret histopathology images. Advances in artificial…”
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Multiscale deep learning framework captures systemic immune features in lymph nodes predictive of triple negative breast cancer outcome in large‐scale studies by Verghese, Gregory, Li, Mengyuan, Liu, Fangfang, Lohan, Amit, Kurian, Nikhil Cherian, Meena, Swati, Gazinska, Patrycja, Shah, Aekta, Oozeer, Aasiyah, Chan, Terry, Opdam, Mark, Linn, Sabine, Gillett, Cheryl, Alberts, Elena, Hardiman, Thomas, Jones, Samantha, Thavaraj, Selvam, Jones, J Louise, Salgado, Roberto, Pinder, Sarah E, Rane, Swapnil, Sethi, Amit, Grigoriadis, Anita

“…The suggestion that the systemic immune response in lymph nodes (LNs) conveys prognostic value for triple‐negative breast cancer (TNBC) patients has not…”
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Abstract PO-014: Deep learning-based segmentation accurately captures histological features in cancer-free lymph nodes of breast cancer patients by Verghese, Gregory, Grigoriadis, Anita, Sethi, Amit, Lohan, Amit, Cherian, Nikhil, Meena, Swati, Chinque, Harry, Liu, Fangfang, Martin, Ed, Clark, Tristan, Gillet, Cheryl, Owen, Julie, Rane, Swapnil

“…Background Histological changes in the cancer-free lymph nodes (LNs) can add to the risk prediction of developing distant metastasis for LN-positive breast…”
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Abstract PS03-09: Age-related normal breast tissue features differ in women with germline BRCA1/2 mutations by Grigoriadis, Anita, Parreno-Centeno, Mario, Chen, Siyuan, Verghese, Gregory, Lips, Esther, Gillett, Cheryl, Jones, Louise, Pinder, Sarah

“…Ageing, a breast cancer risk factor, can be reflected in histologically normal breast tissue (NBT). However, the relationship between age-related histological…”
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Abstract 6233: A deep learning pipeline to capture the prognostic immune responses in lymph nodes of breast cancer patients by Verghese, Gregory, Li, Mengyuan, Lohan, Amit, Cherian, Nikhil, Rane, Swapnil, Liu, Fangfang, Shah, Aekta, Gazinska, Pat, Thavaraj, Selvam, Sethi, Amit, Grigoriadis, Anita

“…Capturing tumor infiltrating leucocytes (TILS) and systemic immune responses in breast cancer informs disease progression and optimal treatment management. We…”
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Abstract P5-01-01: Multiscale Deep Learning framework to capture systemic immune features in lymph nodes predictive of triple negative breast cancer outcome by Verghese, Gregory, Li, Mengyuan, Liu, Fangfang, Lohan, Amit, Cherian, Nikhil, Gazinska, Patrycja, Shah, Aekta, Oozeer, Aasiyah, Gillett, Cheryl, Alberts, Elena, Hardiman, Thomas, Salgado, Roberto, Jones, Samantha, Jones, Louise, Thavaraj, Selvam, Pinder, Sarah E., Rane, Swapni, Sethi, Amit, Grigoriadis, Anita

“…Systemic immune responses in lymph nodes (LN) convey significant prognostic value for breast cancer patients, which can inform disease progression and optimal…”
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Deep Multi-Scale U-Net Architecture and Label-Noise Robust Training Strategies for Histopathological Image Segmentation by Kurian, Nikhil Cherian, Lehan, Amit, Verghese, Gregory, Dharamshi, Nimish, Meena, Swati, Li, Mengyuan, Liu, Fangfang, Gillet, Cheryl, Rane, Swapnil, Grigoriadis, Anita, Sethi, Amit

“…Although the U-Net architecture has been extensively used for the segmentation of medical images, we address two of its shortcomings in this work. Firstly, the…”
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Deep Multi-Scale U-Net Architecture and Label-Noise Robust Training Strategies for Histopathological Image Segmentation by Kurian, Nikhil Cherian, Lohan, Amit, Verghese, Gregory, Dharamshi, Nimish, Meena, Swati, Li, Mengyuan, Liu, Fangfang, Gillet, Cheryl, Rane, Swapnil, Grigoriadis, Anita, Sethi, Amit

“…Although the U-Net architecture has been extensively used for segmentation of medical images, we address two of its shortcomings in this work. Firstly, the…”
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