Tu.P3.E-6
WHO BENEFITS FROM SINUS SURGERY? COMPARING GENERATIVE AI AND SUPERVISED MACHINE LEARNING FOR PREDICTING SURGICAL OUTCOMES IN CHRONIC RHINOSINUSITIS
Sayeed Shafayet Chowdhury, Indiana University Indianapolis; Snehasis Mukhopadhyay, Purdue University; Shiaofen Fang, Indiana University Indianapolis; Vijay R. Ramakrishnan, Indiana University School of Medicine
Session:
Tu.P3: Medical Imaging and Clinical Decision Support Systems Poster
Track:
Biomedical Signal Processing
Location:
Hall E; Bay E, Board #6
Presentation Time:
Tuesday, 28 July, 16:30 - 18:30
Presentation
Discussion
Resources
No resources available.
Session Tu.P3
Tu.P3.E-1: MULTI-LLM COLLABORATIVE MRI REPORT GENERATION FOR VISUAL INSTRUCTION TUNING IN BRAIN ONCOLOGY
Sinyoung Ra, Jonghun Kim, Hyunjin Park, Sungkyunkwan University
Tu.P3.E-2: ENHANCING CYTOLOGICAL STAINING-FREE IMAGE CLASSIFICATION WITH ROBUST VISION TRANSFORMERS
Hugo S. Oliveira, Faculty of Sciences of University of Porto, Faculty of Engineering of University of Porto, Institute for Systems and Computer Engineering, Technology and Science (INESC TEC); Pedro C. Carvalho, Faculty of Sciences of University of Porto; Raquel L. Monteiro, Faculty of Medicine of University of Porto; Daniela Ferreira-Santos, Institute for Systems and Computer Engineering, Technology and Science (INESC TEC); Tania Pereira, Faculty of Engineering of University of Porto, Institute for Systems and Computer Engineering, Technology and Science (INESC TEC); Raphaël F. Canadas, Faculty of Medicine University of Porto, RISE-HEALTH; Hélder P. Oliveira, Faculty of Sciences of University of Porto, Institute for Systems and Computer Engineering, Technology and Science (INESC TEC)
Tu.P3.E-3: ProtoTopic: Prototypical Network for Few-Shot Medical Topic Modeling
Martin Licht, Sara Ketabi, Farzad Khalvati, University of Toronto
Tu.P3.E-4: Hybrid Multi-Dimensional MRI Prostate Cancer Detection via Hadamard Network-Based Bias Correction and Residual Networks
Emadeldeen Hamdan, University of Illinois Chicago; Gorkem Durak, Muhammed Enes TASCI, Northwestern University; Abel Lorente Campos, Aritrick Chatterjee, Roger Engelmann, Gregory Karczmar, Aytekin Oto, University of Chicago; Ahmet Enis Cetin, University of Illinois Chicago; Ulas Bagci, Northwestern University
Tu.P3.E-5: CAUSAL SUBSPACE LEARNING WITH COUNTERFACTUAL REGULARIZATION FOR MEDICAL IMAGE ANALYSIS
Huanlong Gao, Xuelei He, Zechen Zheng, Xiaowei Zhao, Xiaowei He, Northwest University
Tu.P3.E-6: WHO BENEFITS FROM SINUS SURGERY? COMPARING GENERATIVE AI AND SUPERVISED MACHINE LEARNING FOR PREDICTING SURGICAL OUTCOMES IN CHRONIC RHINOSINUSITIS
Sayeed Shafayet Chowdhury, Indiana University Indianapolis; Snehasis Mukhopadhyay, Purdue University; Shiaofen Fang, Indiana University Indianapolis; Vijay R. Ramakrishnan, Indiana University School of Medicine
Tu.P3.E-7: Exploiting Wavelet Scattering and Other Spectral Features in Tri-Modal Cross-Attention Framework for Oral Cancer Detection
Rantu Buragohain, Ashutosh Verma, Karan Nathwani, Indian Institute of Technology Jammu; Parmod Kalsotra, Government Medical College, Jammu
Tu.P3.E-8: SCLERA SEGMENTATION USING U-NET LITE IN A RASPBERRY PI 5
Cristina Elizabeth Reyes Soto, Rigoberto Martinez Mendez, Adriana Herlinda Vilchis Gonzalez, Universidad Autónoma del Estado de México; Azucena Eunice Jiménez Corona, Universidad Popular del Estado de Tlaxcala; Zeus Tlaltecutli Dominguez Vega, Universidad Nacional Autónoma de México; Alexandra Estela Soto Piña, Universidad Autónoma del Estado de México
Tu.P3.E-9: IMPROVING RARE DISEASE DIAGNOSIS VIA CONTRASTIVE METRIC LEARNING FOR HPO PHENOTYPE EXTRACTION
, ; Rishi Ananth, Jyun-Ping Kao, Harvard Medical School and Massachusetts General Brigham; Wooyong Choi, Sanggoo Kang, MedySapiens; Jonghye Woo, Harvard Medical School and Massachusetts General Brigham
Tu.P3.E-10: AOASNet: A MODEL EXPLORING THE CORRELATION OF THE ATTENTION MODULES IS USED FOR MEDICAL IMAGE CLASSIFICATION
Zechen Zheng, Yuxin Jin, Huanlong Gao, Xuelei He, Xiaowei He, Northwest University(China)
Tu.P3.E-11: PERFORMANCE EVALUATION OF QUANTIZED MACHINE LEARNING MODELS FOR REAL-TIME POLYP DETECTION
Gustavo Novack Viana Lima, Carlos Eduardo Gonçalves de Oliveira, Davi de Jesus Teixeira, Rian de Souza Santos, Ricardo Franco, Federal University of Goiás
Tu.P3.E-12: Data-Efficient Spinal Disease Classification via Fusion of Explicit Part-Aware Shape Subspace Features and Implicit Learned Features
Chiawei Liu, Tsuguhiro Tsukumo, Kota Suto, University of Tsukuba; Tomoyuki Asada, Hospital for Special Surgery; Kousei Miura, Hideki Kadone, Naoya Kikuchi, Yasuhiro Homma, Masashi Yamazaki, Sandra Puentes, Naoto Ienaga, Kazuhiro Fukui, Yoshihiro Kuroda, University of Tsukuba
Tu.P3.E-13: MULTIMODAL DEEP LEARNING FOR REDUCED LEFT VENTRICULAR EJECTION FRACTION SCREENING USING ECG IMAGES, CHEST RADIOGRAPHS, AND LLM-EXTRACTED LABELS
Felipe Dias, Diego Cardenas, Estela Ribeiro, Ramon Moreno, Marina Rebelo, Jose Krieger, Marco Gutierrez, Heart Institute
Tu.P3.E-14: CROSS-MODAL ZERO-SHOT MRI SUPER-RESOLUTION VIA AUXILIARY MODALITY GUIDANCE
Feng Xu, Kai Pan, Southern University of Science and Technology; Pujin Cheng, Li Lin, University of Hong Kong; Qiyu Peng, Shenzhen Bay Laboratory; Xiaoying Tang, Southern University of Science and Technology
Tu.P3.E-15: Region-Affinity Attention for Whole-Slide Breast Cancer Classification in Deep Ultraviolet Imaging
Nagur Shareef Shaik, Teja Krishna Cherukuri, Dong Hye Ye, Georgia State University
Tu.P3.E-16: Optimizing Signal Preprocessing for Acoustic Tissue Classification in Needle Guided Procedures
Karol Strama, Jakub Gawron, AGH University of Krakow; Katharina Steeg, University Hospital Giessen; Dominik Rzepka, AGH University of Krakow / Medsun labs sp. z o. o.; Michael Friebe, AGH University of Krakow
Tu.P3.E-17: An Interpretable framework for Brain tumor detection integrating a single shot detector and vision language model (VLM)
Okib Ul Islam, Ayomide Bonojo, Md Mahmudur Rahman, Fahmi Khalifa, Morgan State University
Tu.P3.E-18: PREDICTION OF MULTIPLE DISTINCT POST MORTEM NEUROPATHOLOGY USING IN VIVO BRAIN MRI AND AUTOMATED MACHINE LEARNING
Christopher Patterson, Tamoghna Chattopadhyay, Emma J. Gleave, Sophia Thomopoulos, Paul M. Thompson, University of Southern California
Tu.P3.E-19: MedGate-Fusion: Integrating First-Encounter Semantic Narratives and Physiological Biomarkers for Prospective Stroke Risk Stratification
Hemn Khdr, Zahra Shakeri, Mohammad Noaeen, Karim Keshavjee, Dalla Lana School of Public Health, University of Toronto; Aziz Aziz Guergachi, Toronto Metropolitan University
Tu.P3.E-20: Curriculum Learning for Low-Dose CT Image Reconstruction
Ricardo Ornelas, Roummel Marcia, University of California, Merced
Tu.P3.E-21: A FINE-TUNING METHOD BASED ON CROSS-PATIENT SIMILARITY
Xuyang Zhao, RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences; Toshihisa Tanaka, Tokyo University of Agriculture and Technology
Tu.P3.E-22: Prototypical Contrastive Learning based Dual-color Magnetic Particle Imaging Guided by Nonlinear Harmonic Priors
Guanghui Li, Xueying Liu, Haicheng Du, Siyao Wang, Jie Tian, Yu An, Beihang University
Tu.P3.E-23: Enhancing Pathology Image Captions via Contrastive and Generative Learning for Multimodal Classification
Jiayi Wu, Jingmin Xin, National Key Laboratory of Human-Machine Hybrid Augmented Intelligence and Institute of Artificial Intelligence and Robotics
Tu.P3.E-24: BIDRIECTIONAL LSTM DENOISING FOR NYSTAGMUS FROM SMARTPHONE VIDEOS WITH OPENFACE EYE TRACKING
Benjamin Duvieusart, Diego Kaski, Terence S. Leung, University College London
Resources
No resources available.