[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127195-en":3,"doc-seo-127195-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127195,549768072016,"River Wang","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Robot-Enabled Machine Learning-Based Diagnosis of Gastric Cancer Polyps Using Partial Surface Tactile Imaging","Aiming to overcome limitations of endoscopic diagnosis of Advanced Gastric Cancer (AGC) tumors, the work introduces a Vision-based Tactile Sensor (VTS) and a complementary machine learning approach to classify tumors from textural features. A seven-DoF robotic manipulator and custom additively manufactured AGC tumor phantoms enable automated data collection that mitigates data scarcity and bias. A model trained on synthetic data is evaluated and compared with baseline ML methods under mixed morphologies and partial sensor contact.","Accepted for publication at the 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)   \nRobot-Enabled Machine Learning-Based Diagnosis of Gastric Cancer Polyps Using Partial Surface Tactile Imaging  \nSiddhartha Kapuria* 1 , Jeff Bonyun* 1 , Yash Kulkarni 1 , Naruhiko Ikoma2 , Sandeep Chinchali 1 ,  \nand Farshid Alambeigi 1  \narXiv :2408 .01554v1 [ cs .RO] 2 Aug 2024  \nAbstract—In this paper, to collectively address the existing limitations on endoscopic diagnosis of Advanced Gastric Cancer (AGC) Tumors, for the first time, we propose (i) utilization and evaluation of our recently developed Vision-based Tactile Sensor (VTS), and (ii) a complementary Machine Learning (ML) algorithm for classifying tumors using their textural features. Leveraging a seven DoF robotic manipulator and unique custom-designed and additively-manufactured realistic AGC tumor phantoms, we demonstrated the advantages of automated data collection using the VTS addressing the problem of data scarcity and biases encountered in traditional ML-based approaches. Our synthetic-data-trained ML model was successfully evaluated and compared with traditional ML models utilizing various statistical metrics even under mixed morphological characteristics and partial sensor contact.  \nI. INTRODUCTION  \nGastric cancer (GC) is the fifth most commonly diagnosed cancer worldwide and the fourth leading cause of cancerrelated mortality [1] . A major contributor to this challenge is the fact that a substantial portion — up to 62% — of GC cases are detected at advanced stages, contributing to poorer overall survival rates compared to cases identified at early stages [2] . Upper endoscopy is the primary method for the initial detection of GC lesions as it allows for an inside view of the gastric tract lining where tumors originate. At the advanced GC (AGC) stages, tumors have infiltrated the muscularis propria [3] and can be identified and classified through their morphological characteristics (i.e., their geometry and texture) visible through the images provided by an endoscope. Borrmann classification [3] is a common approach used by clinicians to morphologically classify GC polyps into four types of polypoid (Type 1), fungating (Type 2), ulcerated (Type 3), and infiltrating or Flat (Type 4) (see Fig. 2) . Nevertheless, inter-class variance of each type of polyps and solely relying on morphology of the GC polyps in Borrmann classification has resulted in a high-degree of disagreement and inconsistency in decisionmaking among clinicians [4] . Therefore, long-term specific training and experience is needed to detect GC properly  \n*Authors had equal contribution to this work  \nThis research was supported by the National Cancer Institute of the National Institutes of Health under Award Number R21CA280747  \n1 Siddhartha Kapuria, Jeff Bonyun, Yash Kulkarni, Sandeep Chinchali, and Farshid Alambeigi are with Texas Robotics, University of Texas at Austin, Austin, TX, [USA. email:](USA. email: {skapuria)[ {](USA. email: {skapuria)[skapuria](USA. email: {skapuria), jbonyun, kulkarni.yash08, [sandeepc](sandeepc}@utexas.edu)[}](sandeepc}@utexas.edu)[@utexas.edu](sandeepc}@utexas.edu), and [farshid.alambeigi@austin.utexas.edu](farshid.alambeigi@austin.utexas.edu)  \n2Naruhiko Ikoma is with the Department of Surgical Oncology, Division of Surgery, The University of Texas MD Anderson Cancer Center, Houston,  \nTX, USA, [77030. email:](77030. email:) [nikoma@mdanderson.org](nikoma@mdanderson.org)  \nFig. 1: Experimental Setup including: (1) KUKA LBR Med 14 R820 (KUKA AG); (2) Raspberry Pi 4 Model B; (3) 3D printed mounting plate for the tumor phantoms; (4) Top view of HySenSe sensor showing all components; (5) Example CAD model of synthetic AGC polyp phantom; (6) Example partial textural image output of AGC tumor phantom. Figure also shows the defined reference frames R: Robot/World, B: Robot Flange, C: Camera, T: Target, and H: HySenSe base. using endoscopic image","cbCaicj7uABpi1LY","https://ap.wps.com/l/cbCaicj7uABpi1LY","pdf",4669350,1,6,"English","en",105,"# Abstract\n# I. Introduction\n## Background: gastric cancer and advanced-stage detection\n## Morphological classification and its clinical variability\n## Challenges for endoscopic imaging and diagnosis\n## Prior AI/ML approaches and data limitations","[{\"question\":\"What problem does this study target in gastric cancer diagnosis?\",\"answer\":\"It targets limitations in endoscopic diagnosis of Advanced Gastric Cancer tumors, especially the need for more reliable classification despite imaging constraints and dataset limitations.\"},{\"question\":\"How does the Vision-based Tactile Sensor (VTS) contribute to the method?\",\"answer\":\"The VTS is used for automated tactile data collection, providing textural information to support tumor classification while addressing data scarcity and bias.\"},{\"question\":\"How is the machine learning model trained and evaluated?\",\"answer\":\"The model is trained using synthetic data generated from custom tumor phantoms and is evaluated against traditional ML approaches using multiple statistical metrics, including cases with mixed morphology and partial sensor contact.\"}]","Robot-Enabled Machine Learning-Based Diagnosis of Gastric Cancer Polyps Using Partial Surface Tactile Imaging | 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problem does this study target in gastric cancer diagnosis?","Question",{"text":75,"@type":76},"It targets limitations in endoscopic diagnosis of Advanced Gastric Cancer tumors, especially the need for more reliable classification despite imaging constraints and dataset limitations.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the Vision-based Tactile Sensor (VTS) contribute to the method?",{"text":80,"@type":76},"The VTS is used for automated tactile data collection, providing textural information to support tumor classification while addressing data scarcity and bias.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the machine learning model trained and evaluated?",{"text":84,"@type":76},"The model is trained using synthetic data generated from custom tumor phantoms and is evaluated against traditional ML approaches using multiple statistical metrics, including cases with mixed morphology and partial sensor 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