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Prevalence and predictors of data and code sharing in the medical and health sciences: systematic review with meta-analysis of individual participant data
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From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment
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Abstract P2-11-08: Multimodal Prediction of Breast Cancer Recurrence Assays and Risk of Recurrence
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histolab: A Python library for reproducible Digital Pathology preprocessing with automated testing
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Deep learning-enabled virtual histological staining of biological samples
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Deep learning generates synthetic cancer histology for explainability and education
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MONAI: An open-source framework for deep learning in healthcare
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RetCCL: Clustering-guided contrastive learning for whole-slide image retrieval
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TIAToolbox as an end-to-end library for advanced tissue image analytics
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Transformer-based unsupervised contrastive learning for histopathological image classification
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Multiple Instance Learning for Digital Pathology: A Review on the State-of-the-Art, Limitations & Future Potential
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Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer
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Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images
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Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology
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Building Tools for Machine Learning and Artificial Intelligence in Cancer Research: Best Practices and a Case Study with the PathML Toolkit for Computational Pathology
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SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
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Benchmarking artificial intelligence methods for end-to-end computational pathology
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Explainable artificial intelligence (XAI) in deep learning-based medical image analysis
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The impact of site-specific digital histology signatures on deep learning model accuracy and bias
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PathML: A unified framework for whole-slide image analysis with deep learning
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Alias-Free Generative Adversarial Networks
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TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classication
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Deep learning in histopathology: the path to the clinic
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Deep learning detects genetic alterations in cancer histology generated by adversarial networks
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Second opinion needed: communicating uncertainty in medical machine learning
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Deep learning prediction of BRAF-RAS gene expression signature identifies noninvasive follicular thyroid neoplasms with papillary-like nuclear features
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A deep learning model to predict RNA-Seq expression of tumours from whole slide images
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Clinical-grade Detection of Microsatellite Instability in Colorectal Tumors by Deep Learning.
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Novel artificial intelligence system increases the detection of prostate cancer in whole slide images of core needle biopsies
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PyHIST: A Histological Image Segmentation Tool
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Data-efficient and weakly supervised computational pathology on whole-slide images
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fastai: A Layered API for Deep Learning
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Cellpose: a generalist algorithm for cellular segmentation
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Analyzing and Improving the Image Quality of StyleGAN
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Pan-cancer image-based detection of clinically actionable genetic alterations
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Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis
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Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
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Segment Integrated Gradients: Better attributions through regions
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Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer
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Deep learning with multimodal representation for pancancer prognosis prediction
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The need for uncertainty quantification in machine-assisted medical decision making
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DeepFocus: Detection of out-of-focus regions in whole slide digital images using deep learning
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Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning
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Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks
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A disciplined approach to neural network hyper-parameters: Part 1 - learning rate, batch size, momentum, and weight decay
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Attention-based Deep Multiple Instance Learning
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UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
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Axiomatic Attribution for Deep Networks
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QuPath: Open source software for digital pathology image analysis
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Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
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Structure-Preserving Color Normalization and Sparse Stain Separation for Histological Images
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TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
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Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
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Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
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The Cancer Genome Atlas Pan-Cancer analysis project
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A method for normalizing histology slides for quantitative analysis
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Human Papillomavirus and Head and Neck Squamous Cell Carcinoma: Recent Evidence and Clinical Implications
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VIPS - a highly tuned image processing software architecture
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Prevalence of histological findings of human papillomavirus (HPV) in oral and oropharyngeal squamous cell carcinoma biopsies: preliminary study
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Color Transfer between Images
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Lanczos Filtering in One and Two Dimensions
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cuCIM - A GPU image I/O and processing library
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“Segmentation Models Pytorch,”
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“Style Guide for Python Code,”
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A Threshold Selection Method from Gray-Level Histograms
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Distributed under Creative Commons Cc-by 4.0 Scikit-image: Image Processing in Python
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tumor-segmentation-v1 (Revision 666fa9d)
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UMAP plot of post-convolutional layer activations, calculated using the final trained model, for all images in the TCGA test set
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“Google Python Style Guide.”
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Feature space visualization for the tile-based and multiple-instance learning models. (a) UMAP plot of postconvolutional layer activations, calculated using the final trained model
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“Pylint-code analysis for Python.”
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“Mypy-optional static typing for Python.”
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breast-er-gan-v1 (Revision db36196)
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lung-adeno-squam-v1 (Revision dade98a)
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“neptune.ai: experiment tracking and model registry.”
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thyroid-brs-v1 (Revision 17d17d8)
