Wyniki wyszukiwania dla: ENDOSCOY
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Granular cell tumor, NOS - Female, 25 - Tissue image [5050730010513441]
Dane BadawczeThis is the histopathological image of COLON tissue sample obtained in Medical University Gdańsk and deposited in ZMDL-GUMED. The sample image was taken using: Pannoramic 250 3DHistech slide scanner (20x magnification) and saved to DICOM format.
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Granular cell tumor, NOS - Female, 68 - Tissue image [505073001051661]
Dane BadawczeThis is the histopathological image of ESOPHAGUS tissue sample obtained in Medical University Gdańsk and deposited in ZMDL-GUMED. The sample image was taken using: Pannoramic 250 3DHistech slide scanner (20x magnification) and saved to DICOM format.
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Granular cell tumor, NOS - Female, 25 - Tissue image [5050730010511981]
Dane BadawczeThis is the histopathological image of COLON tissue sample obtained in Medical University Gdańsk and deposited in ZMDL-GUMED. The sample image was taken using: Pannoramic 250 3DHistech slide scanner (20x magnification) and saved to DICOM format.
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Granular cell tumor, NOS - Female, 68 - Tissue image [5050730010514691]
Dane BadawczeThis is the histopathological image of ESOPHAGUS tissue sample obtained in Medical University Gdańsk and deposited in ZMDL-GUMED. The sample image was taken using: Pannoramic 250 3DHistech slide scanner (20x magnification) and saved to DICOM format.
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Granular cell tumor, NOS - Female, 25 - Tissue image [5050730010511731]
Dane BadawczeThis is the histopathological image of COLON tissue sample obtained in Medical University Gdańsk and deposited in ZMDL-GUMED. The sample image was taken using: Pannoramic 250 3DHistech slide scanner (20x magnification) and saved to DICOM format.
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Granular cell tumor, NOS - Female, 55 - Tissue image [6060730013003681]
Dane BadawczeThis is the histopathological image of ESOPHAGUS tissue sample obtained in Medical University Gdańsk and deposited in ZMDL-GUMED. The sample image was taken using: Pannoramic 250 3DHistech slide scanner (20x magnification) and saved to DICOM format.
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Granular cell tumor, NOS - Female, 25 - Tissue image [5050730010514171]
Dane BadawczeThis is the histopathological image of COLON tissue sample obtained in Medical University Gdańsk and deposited in ZMDL-GUMED. The sample image was taken using: Pannoramic 250 3DHistech slide scanner (20x magnification) and saved to DICOM format.
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Granular cell tumor, NOS - Female, 25 - Tissue image [5050730010514291]
Dane BadawczeThis is the histopathological image of COLON tissue sample obtained in Medical University Gdańsk and deposited in ZMDL-GUMED. The sample image was taken using: Pannoramic 250 3DHistech slide scanner (20x magnification) and saved to DICOM format.
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Granular cell tumor, NOS - Female, 25 - Tissue image [5050730010512521]
Dane BadawczeThis is the histopathological image of COLON tissue sample obtained in Medical University Gdańsk and deposited in ZMDL-GUMED. The sample image was taken using: Pannoramic 250 3DHistech slide scanner (20x magnification) and saved to DICOM format.
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Dependable Integration of Medical Image Recognition Components
PublikacjaComputer driven medical image recognition may support medical doctors in the diagnosis process, but requires high dependability considering potential consequences of incorrect results. The paper presentsa system that improves dependability of medical image recognition by integration of results from redundant components. The components implement alternative recognition algorithms of diseases in thefield of gastrointestinal endoscopy....
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Real-Time Gastrointestinal Tract Video Analysis on a Cluster Supercomputer
PublikacjaThe article presents a novel approach to medical video data analysis and recognition. Emphasis has been put on adapting existing algorithms detecting le- sions and bleedings for real time usage in a medical doctor's office during an en- doscopic examination. A system for diagnosis recommendation and disease detec- tion has been designed taking into account the limited mobility of the endoscope and the doctor's requirements. The...
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The Influence of Selecting Regions from Endoscopic Video Frames on The Efficiency of Large Bowel Disease Recognition Algorithms
PublikacjaThe article presents our research in the field of the automatic diagnosis of large intestine diseases on endoscopic video. It focuses on the methods of selecting regions of interest from endoscopic video frames for further analysis by specialized disease recognition algorithms. Four methods of selecting regions of interest have been discussed: a. trivial, b. with the deletion of characteristic, endoscope specific additions to the...