Abstract: The present disclosure provides a multi-manifold embedding learning method, which includes steps as follows. The ID training data are used to train the multi-manifold embedding learning model, and then the parameters of the multi-manifold embedding learning model are frozen to obtain the trained multi-manifold embedding learning model; the test data are fed to the trained multi-manifold embedding learning model, so as to use a threshold to distinguish out-of-distribution samples from ID samples.

Abstract: A method for unseen emotion class recognition comprises: receiving, with an emotion recognition model, a speech sample to be tested; calculating, with an encoder, a sample embedding to be tested of the speech sample to be tested; calculating a first distance metric between the sample embedding to be tested and a first registered emotion category representation, and a second distance metric between the sample embedding to be tested and a second registered emotion category representation, wherein the second registered emotion category is not included in a plurality of basic emotion categories; and determining an emotion category of the speech sample to be tested according to the first distance metric and the second distance metric.

Abstract: This application relates generally to automated systems and associated methods for identifying hematological abnormalities. An automated system can include at least one processor that, in operation, is configured to: receive, from a flow cytometer, a flow cytometry data matrix characterizing a tube that is associated with a sample; convert the flow cytometry data matrix into a high dimensional vector; produce a single sample high dimensional vector including a concatenation of multiple high dimensional vectors associated with the sample, wherein the multiple high dimensional vectors comprise the tube high dimensional vector; assemble a training data set including multiple sample high dimensional vectors; receive, from a datastore, outcome information including respective labels associated with each of the multiple sample high dimensional vectors; and train a classifier based on the training data set and the outcome information.

Abstract: Introduced here is an approach to improving the automatic identification of hematological malignancies by taking advantage of established databases through transfer learning. At a high level, this approach attempts to address the cross-domain gap by preserving knowledge of the source domain for better optimization of the target domain.

Abstract: Introduced here is an approach to improving the automatic identification of hematological diseases using computer-implemented models that are trained to rapidly distinguish between different collections of immunophenotypes that represent different disease types or disease states. Understanding the different patterns of immunophenotype collections contained in a given sample may permit a proposed diagnosis for a given hematological disease to be produced for the corresponding patient. For example, the proposed diagnoses may be output by a classification model based on the distribution of immunophenotypes across the given sample.

Abstract: This application relates generally to a computer implemented method comprising: receiving a medical record data from a patient, wherein said record comprising a static attribute and a time dependent progression attribute; processing the time dependent progression attributes of medical record data using a trained neural network to into time-series representation, and converting the static attributes into static variables; combining the time-series representation and static variables to multiple vectors; providing a prognosis outcome by a trained classifier using said multiple vectors; wherein the neural network is trained by steps of (a) assembling a training data set comprising a retrospective collection of patients' medical record data wherein said record data comprising collected number of static attributes, time dependent progression attributes and patients' mortality and relapse outcomes; (b) processing the time dependent progression attributes of the training data set using a neural network to convert th

Abstract: This application relates generally to automated systems and methods for classifying subtypes of leukemia cells and other applications therefrom.