18544886. IMAGE PROCESSING DEVICE, IMAGE PROCESSING METHOD, AND STORAGE MEDIUM simplified abstract (NEC Corporation)
Contents
- 1 IMAGE PROCESSING DEVICE, IMAGE PROCESSING METHOD, AND STORAGE MEDIUM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 IMAGE PROCESSING DEVICE, IMAGE PROCESSING METHOD, AND STORAGE MEDIUM - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
IMAGE PROCESSING DEVICE, IMAGE PROCESSING METHOD, AND STORAGE MEDIUM
Organization Name
Inventor(s)
Kazuhiro Watanabe of Tokyo (JP)
IMAGE PROCESSING DEVICE, IMAGE PROCESSING METHOD, AND STORAGE MEDIUM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18544886 titled 'IMAGE PROCESSING DEVICE, IMAGE PROCESSING METHOD, AND STORAGE MEDIUM
Simplified Explanation
The image processing device X includes an acquisition means X and a lesion detection means X. The acquisition means X acquires an endoscopic image obtained by photographing an examination target by a photographing unit provided in an endoscope. The lesion detection means X detects a lesion based on a selection model which is selected from a first model and a second model, the first model being configured to make an inference regarding a lesion of the examination target based on a predetermined number of endoscopic images, the second model being configured to make an inference regarding a lesion of the examination target based on a variable number of endoscopic images. Besides, the lesion detection means X changes a parameter to be used for detection of the lesion based on a non-selection model that is the first model or the second model other than the selection model.
- The device includes an acquisition means for obtaining endoscopic images and a lesion detection means for detecting lesions in the examination target.
- The lesion detection means utilizes different models to infer the presence of lesions based on a predetermined or variable number of endoscopic images.
- The device can adjust detection parameters based on the selected model to improve lesion detection accuracy.
Potential Applications
This technology can be applied in medical settings for early detection of lesions during endoscopic examinations. It can assist healthcare professionals in identifying abnormalities in patients.
Problems Solved
1. Early detection of lesions during endoscopic examinations. 2. Improving accuracy in lesion detection using different inference models.
Benefits
1. Enhanced lesion detection capabilities. 2. Potential for early diagnosis and treatment of medical conditions. 3. Improved efficiency in endoscopic examinations.
Potential Commercial Applications
"Advanced Lesion Detection Technology for Endoscopic Examinations"
Possible Prior Art
Prior art may include existing image processing devices for lesion detection in medical imaging, but specific details would need to be researched to determine direct comparisons.
Unanswered Questions
How does this technology compare to existing lesion detection systems in terms of accuracy and efficiency?
This article provides information on the device's capabilities but does not directly compare its performance to other systems.
What are the potential limitations or challenges in implementing this technology in clinical practice?
While the benefits are outlined, potential obstacles or drawbacks to using this technology in real-world medical settings are not addressed.
Original Abstract Submitted
The image processing device X includes an acquisition means X and a lesion detection means X. The acquisition means X acquires an endoscopic image obtained by photographing an examination target by a photographing unit provided in an endoscope. The lesion detection means X detects a lesion based on a selection model which is selected from a first model and a second model, the first model being configured to make an inference regarding a lesion of the examination target based on a predetermined number of endoscopic images, the second model being configured to make an inference regarding a lesion of the examination target based on a variable number of endoscopic images. Besides, the lesion detection means X changes a parameter to be used for detection of the lesion based on a non-selection model that is the first model or the second model other than the selection model.
