We.1.4.1
MIA FEWSL: FEW-SHOT SEGMENTATION WITH FOUNDATION MODELS FOR DATA-EFFICIENT WHOLE SLIDE HISTOPATHOLOGY ANNOTATION
Zhen Yao, Lehigh University; Erik Burlingame, Chaitanya Parmar, Brendon Lutnick, Patricia Raciti, Kristopher Standish, Albert Juan Ramon, Brandon Ginley, Johnson and Johnson Innovative Medicine
Session:
We.1.4: Foundation Models and Trustworthy AI in Medical Imaging Oral
Track:
Biomedical Imaging and Image Processing
Location:
Room 701A
Presentation Time:
Wednesday, 29 July, 08:30 - 08:45
Session Co-Chairs:
Lars Gjesteby and João Pedrosa
Presentation
Discussion
Resources
No resources available.
Session We.1.4
We.1.4.1: MIA FEWSL: FEW-SHOT SEGMENTATION WITH FOUNDATION MODELS FOR DATA-EFFICIENT WHOLE SLIDE HISTOPATHOLOGY ANNOTATION
Zhen Yao, Lehigh University; Erik Burlingame, Chaitanya Parmar, Brendon Lutnick, Patricia Raciti, Kristopher Standish, Albert Juan Ramon, Brandon Ginley, Johnson and Johnson Innovative Medicine
We.1.4.2: MULTI-LEVEL ADAPTER FUSION WITH LEARNABLE TEXT ANCHORS FOR FEW-SHOT MEDICAL ANOMALY CLASSIFICATION
Anandharaj D, Nandhalal S, Mirunalini P, Dinesh S, Muthukumar K A, Sri Sivasubramaniya Nadar college of Engineering
We.1.4.3: MODALITY-SPECIFIC EFFICIENT FOUNDATION MODEL COMPRESSION FOR MEDICAL IMAGES
Gurunath Reddy M, Dattesh Shanbhag, GE HealthCare
We.1.4.4: Uncertainty-Aware Distributional Prototypes for Robust Open-Set Semi-Supervised Medical Image Classification
Zilai Yao, Hang Chen, Baiying Lei, Shenzhen University
We.1.4.5: High-Sensitivity Detection and Quantification of Entotic Cell-in-Cell Structures in Cancer Histopathology Using YOLOv8 and Geometric Reasoning
Buket Barkana, Maria Leyba Mesa, The University of Akron; Mikolaj Bieganski, Izabela Młynarczuk-Bialy, Warsaw Medical University
We.1.4.6: Vision-Language Models in Skin Cancer Diagnosis: Accuracy, Fairness, and Interpretability Evaluation Study
Eman Rezk, University of Waterloo; Ahmad Alshrbini, McMaster Children's Hospital; Walaa M. Moursi, Zhou Wang, University of Waterloo
Resources
No resources available.