The field of optical microscopy spans across numerous industries and research domains, ranging from education to healthcare, quality inspection and analysis. Nonetheless, a key limitation often cited by optical microscopists refers to the limit of its lateral resolution (typically defined as ~200nm), with potential circumventions involving either costly external modules (e.g. confocal scan heads, etc) and/or specialized techniques [e.g. super-resolution (SR) fluorescent microscopy]. Addressing these challenges in a normal (non-specialist) context thus remains an aspect outside the scope of most microscope users & facilities. This study thus seeks to evaluate an alternative & economical approach to achieving SR optical microscopy, involving non-fluorescent phase-modulated microscopical modalities such as Zernike phase contrast (PCM) and differential interference contrast (DIC) microscopy. Two in silico deep neural network (DNN) architectures which we developed previously (termed O-Net and Theta-Net) are assessed on their abilities to resolve a custom-fabricated test target containing nanoscale features calibrated via atomic force microscopy (AFM). The results of our study demonstrate that although both O-Net and Theta-Net seemingly performed well when super-resolving these images, they were complementary (rather than competing) approaches to be considered for image SR, particularly under different image signal-to-noise ratios (SNRs). High image SNRs favoured the application of O-Net models, while low SNRs inclined preferentially towards Theta-Net models. These findings demonstrate the importance of model architectures (in conjunction with the source image SNR) on model performance and the SR quality of the generated images where DNN models are utilized for non-fluorescent optical nanoscopy, even where the same training dataset & number of epochs are being used.

Background: Apparent Diffusion Coefficient (ADC) values and Total Diffusion Volume (TDV) from Whole-body diffusion-weighted MRI (WB-DWI) are recognized cancer imaging biomarkers. However, manual disease delineation for ADC and TDV measurements is unfeasible in clinical practice, demanding automation. As a first step, we propose an algorithm to generate fast and reproducible probability maps of the skeleton, adjacent internal organs (liver, spleen, urinary bladder, and kidneys), and spinal canal. Methods: We developed an automated deep-learning pipeline based on a 3D patch-based Residual U-Net architecture that localizes and delineates these anatomical structures on WB-DWI. The algorithm was trained using "soft-labels" (non-binary segmentations) derived from a computationally intensive atlas-based approach. For training and validation, we employed a multi-center WB-DWI dataset comprising 532 scans from patients with Advanced Prostate Cancer (APC) or Multiple Myeloma (MM), with testing on 45 patients. Results: Our weakly-supervised deep learning model achieved an average dice score/precision/recall of 0.66/0.6/0.73 for skeletal delineations, 0.8/0.79/0.81 for internal organs, and 0.85/0.79/0.94 for spinal canal, with surface distances consistently below 3 mm. Relative median ADC and log-transformed volume differences between automated and manual expert-defined full-body delineations were below 10% and 4%, respectively. The computational time for generating probability maps was 12x faster than the atlas-based registration algorithm (25 s vs. 5 min). An experienced radiologist rated the model's accuracy "good" or "excellent" on test datasets. Conclusion: Our model offers fast and reproducible probability maps for localizing and delineating body regions on WB-DWI, enabling ADC and TDV quantification, potentially supporting clinicians in disease staging and treatment response assessment.

No longer must solutions to mathematical problems be written in'X' (insert Python/C /R here). With the increasing uptake of integrating AI and optimisation tools in software, it has never been easier for developers to learn more about these topics without having to completely abandon their tech stack. What's more is that this upskilling can be done from the comfort of a .NET application. ML.NET was first released in 2018. It does exactly what the name implies: Machine Learning in .NET.

With ML.NET and related NuGet packages for TensorFlow you can currently do the following: Here's a Getting started sample on scoring a TensorFlow model which is using the Inception pre-trained TensorFlow model. Transfer Learning on top of a pre-trained TensorFlow model: You can re-use part of an already pre-trained TensorFlow model (such as the Inception pre-trained TensorFlow model) to build a new model trained with additional samples for the final layer, such as trained with new images. For instance, see this Tutorial on how to use Transfer Learning with ML.NET by using an already trained Image Classifier TensorFlow model to build a new custom model to classify images into different categories. However, in the scenario where you want to train with your own images, the Transfer Learning approach can be a bit complex because even without taking into account the code implementation for transfer learning you'll need to find a base TensorFlow model to train on top of it which was originally trained with similar image types to your new images. Here's some specific examples to understand that statement: For instance, the TensorFlow Inception model was trained with photos of may objects, animals, vegetables and people, so you could train the final layer, let's say with photos of'super heroes', and the model will clasify properly images of specific'super heroes'.

Microsoft released ML.NET as a commitment to making machine learning a great and easy experience in .NET. First, let's go over the basics of machine learning. Machine learning is getting computers to make predictions without being explicitly programmed. Machine learning is used to solve problems that are difficult (or impossible) to solve with rules-based programming (e.g., if statements and for loops). For instance, if you were asked to create an application that predicts whether an image has a dog in it or not, you might not know where to start. Similarly, if you were asked to write a function that predicts the price of a shirt based on the description of the shirt, you might start by looking at keywords such as "long sleeves" and "business casual," but you might not know how to build a function to scale that to a few hundred products.

The goal is to be able to make SENTIMENT ANALYSIS prediction/detection of what the user is writing in a very UI interactive app (BLAZOR based) in the client side and running an ML.NET model (Sentiment analysis based on binary-classification) in the server side. From ML.NET perspective, the goal is to optimize the ML.NET model executions in the server by sharing the ML.NET objects used for predictions across Http requests and being able to implement very simple code to be used by the user when predicting, like the following line of code that you could write on any ASP.NET Core controller's method or custom service class: The object predictionEnginePool will be injected in the controller's constructor or into you custom class. Internally, it is optimized so the object dependencies are cached and shared across Http requests with minimum overhead when creating those objects. Blazor allows you to run C# code in the client side, as shown in the architecture figure. For this sample we chose to run the ML.NET model in the server side, so the model is protected within the service.

Microsoft released ML.NET as a commitment to making machine learning a great and easy experience in .NET. First, let's go over the basics of machine learning. Machine learning is getting computers to make predictions without being explicitly programmed. Machine learning is used to solve problems that are difficult (or impossible) to solve with rules-based programming (e.g., if statements and for loops). For instance, if you were asked to create an application that predicts whether an image has a dog in it or not, you might not know where to start. Similarly, if you were asked to write a function that predicts the price of a shirt based on the description of the shirt, you might start by looking at keywords such as "long sleeves" and "business casual," but you might not know how to build a function to scale that to a few hundred products.

We are excited to announce ML.NET 1.2 and updates to Model Builder and the CLI. ML.NET is an open-source and cross-platform machine learning framework for .NET developers. ML.NET also includes Model Builder (a simple UI tool for Visual Studio) and the ML.NET CLI (Command-line interface) to make it super easy to build custom Machine Learning (ML) models using Automated Machine Learning (AutoML). Using ML.NET, developers can leverage their existing tools and skill-sets to develop and infuse custom ML into their applications by creating custom machine learning models for common scenarios like Sentiment Analysis, Price Prediction, Image Classification and more! ML.NET 1.2 is a backwards compatible release with no breaking changes so please update to get the latest changes.

Today, coinciding with //BUILD 2019/ conference, we're thrilled by launching ML.NET 1.0 release! You can read the official ML.NET 1.0 release announcement Blog Post here and get started at the ML.NET site here. In this blog post I'm providing quite a few additional technical details along with my personal vision that you might find interesting, though. This is the first main milestone of a great journey in the open that started on May 2018 when we released ML.NET 0.1 as open source. Since then we've been releasing monthly, 12 preview releases plus this final 1.0 release, as shown in the roadmap below: The diagram above shows the the development in the open of ML.NET, however, as explained below, ML.NET has been internally used by Microsoft for quite a few years and used by other Microsoft products such as Bing Ads, Office, Windows, Azure, etc. ML.NET is an open-source and cross-platform machine learning framework (Windows, Linux, macOS), created by Microsoft, for .NET developers.

Two months ago, at //Build 2018, we released ML.NET 0.1, a cross-platform, open source machine learning framework for .NET developers. We've gotten great feedback so far and would like to thank the community for your engagement as we continue to develop ML.NET together in the open. We are happy to announce the latest version: ML.NET 0.3. This release supports exporting models to the ONNX format, enables creating new types of models with Factorization Machines, LightGBM, Ensembles, and LightLDA, and addressing a variety of issues and feedback we received from the community. The main highlights of ML.NET 0.3 release are explained below.