Image Retrieval Method for Multiscale Objects from Optical Colonoscopy Images
Hirokazu Nosato,Hidenori Sakanashi,Eiichi Takahashi,Masahiro Murakawa,Hiroshi Aoki,Ken Takeuchi,Yasuo Suzuki +6 more
TL;DR: A retrieval method for colonoscopy images that can cope with multiscale objects is proposed that is able to retrieve objects of any visible size and any location at a high level of accuracy.
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Abstract: Optical colonoscopy is the most common approach to diagnosing bowel diseases through direct colon and rectum inspections. Periodic optical colonoscopy examinations are particularly important for detecting cancers at early stages while still treatable. However, diagnostic accuracy is highly dependent on both the experience and knowledge of the medical doctor. Moreover, it is extremely difficult, even for specialist doctors, to detect the early stages of cancer when obscured by inflammations of the colonic mucosa due to intractable inflammatory bowel diseases, such as ulcerative colitis. Thus, to assist the UC diagnosis, it is necessary to develop a new technology that can retrieve similar cases of diagnostic target image from cases in the past that stored the diagnosed images with various symptoms of colonic mucosa. In order to assist diagnoses with optical colonoscopy, this paper proposes a retrieval method for colonoscopy images that can cope with multiscale objects. The proposed method can retrieve similar colonoscopy images despite varying visible sizes of the target objects. Through three experiments conducted with real clinical colonoscopy images, we demonstrate that the method is able to retrieve objects of any visible size and any location at a high level of accuracy.
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Citations
Support System of Cystoscopic Diagnosis for Bladder Cancer Based on Artificial Intelligence
Atsushi Ikeda,Hirokazu Nosato,Yuta Kochi,Yuta Kochi,Takahiro Kojima,Koji Kawai,Hidenori Sakanashi,Hidenori Sakanashi,Masahiro Murakawa,Masahiro Murakawa,Hiroyuki Nishiyama +10 more
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Liver tumour classification using average correction higher order local autocorrelation coefficient and legendre moments
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TL;DR: Classification of tumors as benign or malignant is accomplished using Rough-set classifier based on texture feature extracted using Average Correction Higher Order Local Autocorrelation Coefficients and Legendre moments and accuracy achieved is 90%.
Deep Learning Based Pathologic Images Recognition Upon Invasive Bladder Cancer
TL;DR: A deep learning based prediction model is proposed that can effectively distinguish invasive bladder cancer from non-invasive bladder cancer and achieves good classification performance through continuous optimization of model parameters.
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