View source for 18544857. IMAGE PROCESSING DEVICE, IMAGE PROCESSING METHOD, AND STORAGE MEDIUM simplified abstract (NEC Corporation)

=IMAGE PROCESSING DEVICE, IMAGE PROCESSING METHOD, AND STORAGE MEDIUM=


==Organization Name==

[[:Category:NEC Corporation|NEC Corporation]]


[[Category:NEC Corporation]]

==Inventor(s)==

[[:Category:Kazuhiro Watanabe of Tokyo (JP)|Kazuhiro Watanabe of Tokyo (JP)]][[Category:Kazuhiro Watanabe of Tokyo (JP)]]

[[:Category:Yuji Iwadate of Tokyo (JP)|Yuji Iwadate of Tokyo (JP)]][[Category:Yuji Iwadate of Tokyo (JP)]]

[[:Category:Masahiro Saikou of Tokyo (JP)|Masahiro Saikou of Tokyo (JP)]][[Category:Masahiro Saikou of Tokyo (JP)]]

[[:Category:Akinori Ebihara of Tokyo (JP)|Akinori Ebihara of Tokyo (JP)]][[Category:Akinori Ebihara of Tokyo (JP)]]

[[:Category:Taiki Miyagawa of Tokyo (JP)|Taiki Miyagawa of Tokyo (JP)]][[Category:Taiki Miyagawa 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 18544857 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.

* Acquisition means X acquires endoscopic images taken by a photographing unit in an endoscope.
* Lesion detection means X uses a selection model to detect lesions based on a predetermined or variable number of endoscopic images.
* The device can switch between different models for lesion detection and adjust parameters accordingly.

== Potential Applications ==
This technology can be used in medical settings for early detection of lesions or abnormalities during endoscopic examinations.

== Problems Solved ==
This technology helps in improving the accuracy and efficiency of lesion detection during endoscopic procedures.

== Benefits ==
The device can assist medical professionals in identifying lesions in patients, leading to timely diagnosis and treatment.

== Potential Commercial Applications ==
This technology can be utilized by medical device companies to develop advanced image processing devices for endoscopic procedures.

== Possible Prior Art ==
Prior art may include similar image processing devices used in the medical field for lesion detection during endoscopic examinations.

== Unanswered Questions ==
=== How does the device handle different types of lesions or abnormalities? ===
The abstract does not specify how the device distinguishes between different types of lesions or abnormalities during the detection process.

=== What is the accuracy rate of lesion detection using this technology? ===
The abstract does not provide information on the accuracy rate of lesion detection achieved by the device.


==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.



[[Category:G06T7/00]]
[[Category:A61B1/00]]
[[Category:G06T11/20]]
[[Category:G16H50/50]]