PATENT CLAIM ANALYSIS

Application Number: 15868062
Application Type: Utility
Filing Date: 2018-01
Publication Date: 2018-08
Patent Classification: ["382", "131000"]

Abstract:
Methods and apparatus for automated medical image analysis using deep learning networks are disclosed. In a method of automatically performing a medical image analysis task on a medical image of a patient, a medical image of a patient is received. The medical image is input to a trained deep neural network. An output model that provides a result of a target medical image analysis task on the input medical image is automatically estimated using the trained deep neural network. The trained deep neural network is trained in one of a discriminative adversarial network or a deep image-to-image dual inverse network.

Claim (Index 12):
The apparatus of  claim 11 , wherein the trained deep neural network is an estimator network that directly maps the output model from the input medical image and is trained in the discriminative adversarial network, which includes the estimator network and a discriminator network that distinguishes between estimated output models estimated by the estimator network from input training images and real ground-truth output models, conditioned on the input training images.

Metadata:
- Claim Count in Document: 11.0
- Percentile: 86.0
- Lexical Diversity: 2.12
- Patent Class: 382.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15382414', '15618384', '14709536', '14706108', '15055161']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3511542649845705
- 35 USC 102 Novelty (BERT): 0.5285092587667496
- Combined Prediction Score: 0.3688897643627884
- Mean Citation Score: 294.73993200000007
- Max Citation Score: 334.34225
- Similarity Product: 262.6974565206915

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 0
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test