مورد إلكتروني
Prediction of cancer treatment response from histopathology images through imputed transcriptomics ...
| العنوان: | Prediction of cancer treatment response from histopathology images through imputed transcriptomics ... |
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| المؤلفون: | Hoang, Danh-Tai, Dinstag, Gal, Hermida, C. Leandro, Ben-Zvi, S. Doreen, Elis, Efrat, Caley, Katherine, Sammut, Stephen-John, Sinha, Sanju, Sinha, Neelam, Dampier, H. Christopher, Stossel, Chani, Patil, Tejas, Rajan, Arun, Lassoued, Wiem, Strauss, Julius, Bailey, Shania, Allen, Clint, Redman, Jason, Beker, Tuvik, Jiang, Peng, Golan, Talia, Wilkinson, Scott, Sowalsky, G. Adam, Pine, R. Sharon, Caldas, Carlos, Gulley, L. James, Aldape, Kenneth, Aharonov, Ranit, Stone, A. Eric, Ruppin, Eytan |
| بيانات النشر: | Zenodo |
| سنة النشر: | 2023 |
| المجموعة: | DataCite Metadata Store (German National Library of Science and Technology) |
| مصطلحات موضوعية: | Deep Learning, Machine Learning, Precision Oncology, Histopathology, Whole Slide Image, DeepPT, RNAseq |
| الوصف: | DeepPT codes in the manuscript "Prediction of cancer treatment response from histopathology images through imputed transcriptomics" are uploaded here. Please see the README for details. ----- Introduction: DeepPT (Deep Pathology for Transcriptomics) is a deep learning framework that predicts gene expression from histopathology images. DeepPT consists of 4 main components: 1. Image pre-processing: Split each whole slide image into tiles/patches and select only tiles that contain tissue and exclude them from background. Color normalization was included to minimize staining variation (heterogeneity and batch effects). 2. Feature extraction: Use the pre-trained ResNet50 CNN model to extract image features from the tiles. Through this process, each image tile is represented by a vector of 2,048 derived features (pre-trained ResNet features). 3. Feature compression: Compress the 2,048 pre-trained ResNet features to 512 features using an autoencoder network. This helps to exclude noise, to avoid overfitting, and ... |
| نوع الوثيقة: | software article in journal/newspaper |
| اللغة: | English |
| العلاقة: | https://dx.doi.org/10.5281/zenodo.7912193Test |
| DOI: | 10.5281/zenodo.8242989 |
| الإتاحة: | https://doi.org/10.5281/zenodo.824298910.5281/zenodo.7912193Test https://zenodo.org/record/8242989Test |
| حقوق: | Restricted Access ; info:eu-repo/semantics/restrictedAccess |
| رقم الانضمام: | edsbas.40E57ABD |
| قاعدة البيانات: | BASE |
| DOI: | 10.5281/zenodo.8242989 |
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