Abstract: Systems and methods are provided for classifying an abnormality in a medical image. An input medical image depicting a lesion is received. The lesion is localized in the input medical image using a trained localization network to generate a localization map. The lesion is classified based on the input medical image and the localization map using a trained classification network. The classification of the lesion is output. The trained localization network and the trained classification network are jointly trained.

Abstract: For decision support in a medical therapy, machine learning provides a machine-learned generator for generating a prediction of outcome for therapy personalized to a patient. The outcome prediction may be used to determine dose. To assist in decision support, a regression analysis of the cohort used for machine training relates the outcome from the machine-learned generator to the dose and an actual control time (e.g., time-to-event). The dose that minimizes side effects while minimizing risk of failure to a time for any given patient is determined from the outcome for that patient and a calibration from the regression analysis.

Abstract: An operator cab for a work vehicle includes: a cab enclosure including a roof an operator chair disposed in the cab enclosure; at least one layer of noise-deadening material coupled to the roof; and a headliner coupled to the roof such that the at least one layer of noise-deadening material is at least partially disposed between the headliner and the roof. The headliner includes a noise-reflective material and a noise escape region that comprises at least one opening configured to allow sound waves to pass through the noise escape region into the at least one layer of noise-deadening material and reduce noise reflection by the headliner to the operator chair.

Abstract: Risks of radiation-induced toxicity associated with a radiotherapy treatment of a target region of a patient are predicted. Data associated with a region of interest comprising the target region is received. The received data includes a predefined dose map of the radiotherapy treatment and pre-radiotherapy-treatment imaging data of the region of interest. A trained machine-learning algorithm is applied to the received data. The trained machine-learning algorithm generates at least one toxicity indicator based on the received data. The at least one toxicity indicator is indicative of the risks of the radiation-induced toxicity.

Abstract: Systems and methods are provided for classifying an abnormality in a medical image. An input medical image depicting a lesion is received. The lesion is localized in the input medical image using a trained localization network to generate a localization map. The lesion is classified based on the input medical image and the localization map using a trained classification network. The classification of the lesion is output. The trained localization network and the trained classification network are jointly trained.

Abstract: Systems and methods for personalized sudden cardiac death risk prediction that generates fingerprints of imaging features of cardiac structure and function. One or more fingerprints and clinical data may be used to generate a risk score. The output risk score may be used to predict the time of death in order to select high-risk patients for implantable cardioverter-defibrillator treatment.

Abstract: Systems and methods for reducing false positive detections of malignant lesions are provided. A candidate malignant lesion is detected in one or more medical images, such as, e.g., multi-parametric magnetic resonance images. One or more patches associated with the candidate malignant lesion are extracted from the one or more medical images. The candidate malignant lesion is classified as being a true positive detection of a malignant lesion or a false positive detection of the malignant lesion based on the one or more extract patches using a trained machine learning network. The results of the classification are output.

Abstract: A method for preparing needle coke for ultra-high power (UHP) electrodes from heavy oil is provided. In this method, heavy oil is used as a raw material. The size exclusion chromatography (SEC) is conducted with polystyrene (PS) as a packing material to separate out specific components with a relative molecular weight of 400 to 1,000. The ion-exchange chromatography (IEC) is conducted to remove acidic and alkaline components to obtain a neutral raw material. The neutral raw material is subjected to two-stage consecutive carbonization to obtain green coke, and the green coke is subjected to high-temperature calcination to obtain the needle coke for UHP electrodes. The needle coke has a true density of more than 2.13 g/cm3 and a coefficient of thermal expansion (CTE) of ?1.15×10?6/° C. at 25° C. to 600° C.

Abstract: A method for preparing needle coke for ultra-high power (UHP) electrodes from heavy oil is provided. In this method, heavy oil is used as a raw material. The size exclusion chromatography (SEC) is conducted with polystyrene (PS) as a packing material to separate out specific components with a relative molecular weight of 400 to 1,000. The ion-exchange chromatography (IEC) is conducted to remove acidic and alkaline components to obtain a neutral raw material. The neutral raw material is subjected to two-stage consecutive carbonization to obtain green coke, and the green coke is subjected to high-temperature calcination to obtain the needle coke for UHP electrodes. The needle coke has a true density of more than 2.13 g/cm3 and a coefficient of thermal expansion (CTE) of ?1.15×10?6/° C. at 25° C. to 600° C.

Abstract: Similar pre-stored medical datasets are identified by comparison with a current case dataset. A current case dataset is provided and includes radiological data of a patient. A number of pre-stored medical datasets each including radiological data of other patients are provided. Each case dataset is evaluated according to a predefined AI-based method to obtain a number of definitive features for that case dataset. The definitive features of the current case dataset are compared with the definitive features of each pre-stored medical dataset to identify a number of pre-stored medical datasets most similar to the current case dataset. The identified number of most similar pre-stored medical datasets are output.

Abstract: Preparation methods of a high modulus carbon fiber (HMCF) and a precursor (mesophase pitch (MP)) thereof are provided. The preparation method of MP includes: separating components with a molecular weight distribution (MWD) of 400 to 1,000 from a heavy oil raw material through size-exclusion chromatography (SEC); subjecting the components to ion-exchange chromatography (IEC) to obtain modified feedstock oil, where, the components are passed through macroporous cation-exchange and anion-exchange resins in sequence to remove acidic and alkaline components; and subjecting the modified feedstock oil to thermal polycondensation and carbonization to obtain high-quality MP with prominent spinnability. With high mesophase content, low softening point, low viscosity, and prominent meltability and spinnability, the obtained MP is a high-quality raw material for preparing HMCFs. The obtained MP can be subjected to melt spinning, pre-oxidation, carbonization, and graphitization to obtain an MP-based HMCF.

Abstract: In an method for training artificial intelligence entities (AIE) for abnormality detection, medical imaging data of the human organ is provided as training data having training samples, the medical imaging data including imaging results from different types of imaging techniques for each training sample of the training data, a pre-trained or randomly initialized AIE is provided, and the AIE is trained using the provided training samples. The training may include, for at least one training sample, a first loss function for a sub-structure of the AIE is calculated independently of a first spatial region of the human organ, and, for a training sample, a second loss function for a sub-structure of the AIE is calculated independently of a second spatial region of the human organ. The AIE may be trained using the calculated first loss function and the calculated second loss function.

Abstract: Systems and methods are provided for classifying an abnormality in a medical image. An input medical image depicting a lesion is received. The lesion is localized in the input medical image using a trained localization network to generate a localization map. The lesion is classified based on the input medical image and the localization map using a trained classification network. The classification of the lesion is output. The trained localization network and the trained classification network are jointly trained.

Abstract: A fluid tank that includes a housing. The housing includes a bottom wall, a top wall, and a side wall. The side wall couples the bottom wall to the top wall to define a cavity that receives and houses a liquid. A conduit guides liquid from a fluid source into the cavity. The conduit defines an inlet and an outlet. The outlet couples to the side wall. A vent coupled to the housing and to the conduit. The vent defines a vent inlet coupled to the housing and a vent outlet coupled to the conduit. The vent discharges gas from the housing into the conduit.

Abstract: An operator cab for a work vehicle includes: a cab enclosure including a roof an operator chair disposed in the cab enclosure; at least one layer of noise-deadening material coupled to the roof; and a headliner coupled to the roof such that the at least one layer of noise-deadening material is at least partially disposed between the headliner and the roof. The headliner includes a noise-reflective material and a noise escape region that comprises at least one opening configured to allow sound waves to pass through the noise escape region into the at least one layer of noise-deadening material and reduce noise reflection by the headliner to the operator chair.

Abstract: A computer-implemented method for decoding brain imaging data of individual subjects by using additional imaging data from other subjects includes receiving a plurality of functional Magnetic Resonance Imaging (fMRI) datasets corresponding to a plurality of subjects. Each fMRI dataset corresponds to a distinct subject and comprises brain activation patterns resulting from presentation of a plurality of stimuli to the distinct subject. A group dimensionality reduction (GDR) technique is applied to the example fMRI datasets to yield a low-dimensional space of response variables shared by the plurality of subjects. A model is trained to predict a set of target variables based on the low-dimensional space of response variables shared by all subjects, wherein the set of target variables comprise one or more characteristics of the plurality of stimuli.

Abstract: Systems and methods for reducing false positive detections of malignant lesions are provided. A candidate malignant lesion is detected in one or more medical images, such as, e.g., multi-parametric magnetic resonance images. One or more patches associated with the candidate malignant lesion are extracted from the one or more medical images. The candidate malignant lesion is classified as being a true positive detection of a malignant lesion or a false positive detection of the malignant lesion based on the one or more extract patches using a trained machine learning network. The results of the classification are output.

Abstract: Systems and methods for personalized sudden cardiac death risk prediction that generates fingerprints of imaging features of cardiac structure and function. One or more fingerprints and clinical data may be used to generate a risk score. The output risk score may be used to predict the time of death in order to select high-risk patients for implantable cardioverter-defibrillator treatment.

Abstract: Systems and methods for predicting a patient specific risk of cardiac events for cardiac arrhythmia are provided. A medical image sequence of a heart of a patient is received. Cardiac function features are extracted from the medical image sequence. Additional features are extracted from patient data of the patient. A patient specific risk of a cardiac event is predicted based on the extracted cardiac function features and the extracted additional features.

Abstract: By way of introduction, the present embodiments described below include apparatuses and methods for generating natural language representations of mental content from functional brain images. Given functional imaging data acquired while a subject reads a text passage, a reconstruction of the text passage is produced. Linguistic semantic vector representations are assigned (1301) to words, phrases or sentences to be used as training stimuli. Basis learning is performed (1305), using brain imaging data acquired (1303) when a subject is exposed to the training stimuli and the corresponding semantic vectors for training stimuli, to learn an image basis directly. Semantic vector decoding (1309) is performed with functional brain imaging data for test stimuli and using the image basis to generate a semantic vector representing the test imaging stimuli. Text generation (1311) is then performed using the decoded semantic vector representing the test imaging stimuli.