Self-supervised learning (SSL) has emerged as a promising technique for medical image analysis due to its ability to learn without annotations. However, despite the promising potential, conventional SSL methods encounter limitations, including challenges in achieving semantic alignment and capturing subtle details. This leads to suboptimal representations, which fail to accurately capture the underlying anatomical structures and pathological details. In response to these constraints, we introduce a novel SSL framework OPTiML, employing optimal transport (OT), to capture the dense semantic invariance and fine-grained details, thereby enhancing the overall effectiveness of SSL in medical image representation learning. The core idea is to integrate OT with a cross-viewpoint semantics infusion module (CV-SIM), which effectively captures complex, fine-grained details inherent in medical images across different viewpoints. In addition to the CV-SIM module, OPTiML imposes the variance and covariance regularizations within OT framework to force the model focus on clinically relevant information while discarding less informative features. Through these, the proposed framework demonstrates its capacity to learn semantically rich representations that can be applied to various medical imaging tasks. To validate its effectiveness, we conduct experimental studies on three publicly available datasets from chest X-ray modality. Our empirical results reveal OPTiML's superiority over state-of-the-art methods across all evaluated tasks.

Without further adieu, let's predict the 2020 winners! For each category, we predict the most likely winner along with other nominees sorted by decreasing scores. Keep in mind that these scores aren't supposed to add up to 100. Rather, they are "points" given to the nominee by the underlying Deepnet model on a scale of 0 to 100. Another way to look at this is that the model is telling us how a movie/artist with a given set of characteristics will do in a given award based on 19 years of historical data on that award AND independent of the other nominees for the same award this year.

Following the success of predicting 6 out of 6 for the Oscars last year, we have the bar set high for using Machine Learning to predict the 2019 Oscars winners. This year, however, the results are not as obvious. For some of the top categories, our projected results show ties for who gets to take home the coveted gold statuette. Nevertheless, we are excited to share our predictions and see how the Academy Awards pan out this Sunday! Once again, we apply the standard Machine Learning workflow of collecting and preparing a dataset, building and evaluating models, to ultimately make predictions.

The idea of model fusions is pretty simple: You combine the predictions of a bunch of separate classifiers into a single, uber-classifier prediction, in theory, better than the predictions of its individual constituents. As my colleague Teresa Álverez mentioned in a previous post, however, this doesn't typically lead to big gains in performance. In many cases, OptiML will find something as good or better than any combination you could try by hand. Why waste your time fiddling with combinations of models when you could spend it on doing things that will almost certainly have a more measurable impact on your model's performance, like feature engineering or better yet, acquiring more and better data? Part of the answer here is that looking at a number like "R squared" or "F1-score" is often an overly reductive view of performance.

Our Winter 2018 release presents OptiML, an optimization process for model selection and parametrization that automatically finds the best supervised model to help you solve classification and regression problems. This new resource creates and evaluates hundreds of supervised models (decision trees, ensembles, logistic regression, and deepnets) with multiple configurations to finally return a list of the best models for your data. OptiML helps to avoid the difficult and time-consuming work of hand-tuning multiple supervised algorithms until you find the optimal one that solves your specific problem.

This blog post, the second in a series of 6 posts exploring OptiML, the new feature for automatic model optimization on BigML, focuses on a real-world use case within the healthcare industry: medical appointment "no shows". We will demonstrate how OptiML uses Bayesian parameter optimization to search for the the best performing model for your data. The status of the search is continually updated in the BigML Dashboard and the process yields a list of models ranked by performance, which enables further exploration, evaluation, and prediction tasks. With regards to healthcare expenses, "no show" appointments represent a major expense, estimated to cost hospitals over $150 billion per year. A "no show" is when all the necessary information for a medical appointment has been delivered, yet the patient fails to arrive at the scheduled appointment.

One click and you're done, right? That's the promise of OptiML and automated Machine Learning in general, and to some extent, the promise is kept. No longer do you have to worry about fiddly, opaque parameters of Machine Learning algorithms, or which algorithm works best. We're going to do all of that for you, trying various things in a reasonably clever way until we're fairly sure we've got something that works well for your data. Sounds really exciting, but hold your horses.

One click and you're done, right? That's the promise of OptiML and automated Machine Learning in general, and to some extent, the promise is kept. No longer do you have to worry about fiddly, opaque parameters of Machine Learning algorithms, or which algorithm works best. We're going to do all of that for you, trying various things in a reasonably clever way until we're fairly sure we've got something that works well for your data. Sounds really exciting, but hold your horses.