Abstract: A method may include training a machine learning model to reconstruct, based at least on a first image having a first spatial resolution, a second image having a second spatial resolution lower than the first spatial resolution. The reconstruction may include an iterative up-projection and down-projection of the second image to generate a third image having a third spatial resolution higher than the second spatial resolution. The training may include adjusting the machine learning model to minimize a first error between a target image having a target resolution and the third image and a second error between the second image and a fourth image generated by down-projection of a first up-projection of the second image. The method may also include applying the trained machine learning model to increase a spatial resolution of one or more images. Related methods and articles of manufacture are also disclosed.

Abstract: A method may include training a machine learning model to identify new lesions and/or enlarging lesions that developed within a multitemporal image input between a first timepoint and a second timepoint. The multitemporal image input includes a first image acquired at the first timepoint and a second image acquired at the second timepoint. The machine learning model is trained by at least generating a first representation of the multitemporal image input from the first timepoint to the second timepoint, a second representation of the multitemporal image input from the second timepoint to the first timepoint, a third representation of the multitemporal image input, and a lesion mask identifying the one or more new lesions and/or enlarging lesions. The method also includes applying the trained machine learning model to generate a lesion mask for a patient. Related methods and articles of manufacture are also disclosed.

Abstract: The present invention relates to a compound of formula (I), or a stereoisomer thereof, or a pharmaceutically acceptable salt thereof, and a use thereof in the preparation of a drug for treating diseases related to WEE1 activity.

Abstract: Provided are a new class of compounds having a WEE1 inhibitory effect and as represented by formula I, and the use thereof in the preparation of a drug.

Abstract: Embodiments disclosed herein generally relate to connected machine learning models with joint training for lesion detection. Particularly, aspects of the present disclosure are directed to accessing a three-dimensional magnetic resonance imaging (MRI) image, wherein the three-dimensional MRI image depicts a region of a brain of a subject, wherein the region of the brain includes at least a first type of lesions and a second type of lesions; inputting the three-dimensional MRI image into a machine-learning model comprising a first convolutional neural network and a second convolutional neural network; generating a first segmentation mask for the first type of lesions using the first convolutional neural network that takes as input the three-dimensional MRI image; generating a second segmentation mask for the second type of lesions using the second convolutional neural network that takes as input the three-dimensional MRI image; and outputting the first segmentation mask and the second segmentation mask.

Abstract: Embodiments disclosed herein generally relate to multi-arm machine learning models for lesion detection. Particularly, aspects of the present disclosure are directed to accessing a three-dimensional magnetic resonance imaging (MRI) images. Each of the three-dimensional MRI images depict a same volume of a brain of a subject. The volume of the brain includes at least part of one or more lesions. Each three-dimensional MRI image of the three-dimensional MRI images is processed using one or more corresponding encoder arms of a machine-learning model to generate an encoding of the three-dimensional MRI image. The encodings of the three-dimensional MRI images are concatenated to generate a concatenated representation. The concatenated representation is processed using a decoder arm of the machine-learning model to generate a prediction that identifies one or more portions of the volume of the brain predicted to depict at least part of a lesion.