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Acute Pain Recognition from Facial Expression Videos using Vision Transformers.
Annu Int Conf IEEE Eng Med Biol Soc
July 2024
Pain assessment is significant for patients and clinicians in diagnosis and treatment injuries and disease. It could facilitate a patient's treatment process by monitoring patients' pain levels in an accurate and regular manner. Automated detection of pain from facial expressions is a useful technique to assess pain of patients with communication disabilities.
View Article and Find Full Text PDFBoosting Convolution with Efficient MLP-Permutation for Volumetric Medical Image Segmentation.
IEEE Trans Med Imaging
January 2025
Recently, the advent of Vision Transformer (ViT) has brought substantial advancements in 3D benchmarks, particularly in 3D volumetric medical image segmentation (Vol-MedSeg). Concurrently, multi-layer perceptron (MLP) network has regained popularity among researchers due to their comparable results to ViT, albeit with the exclusion of the resource-intensive self-attention module. In this work, we propose a novel permutable hybrid network for Vol-MedSeg, named PHNet, which capitalizes on the strengths of both convolution neural networks (CNNs) and MLP.
View Article and Find Full Text PDFEfficacy and safety of H-APC in Barrett's esophagus: Italian prospective multicenter study.
Endosc Int Open
February 2025
Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy.
Background And Study Aims: Hybrid argon plasma coagulation (H-APC) is a novel technique for ablation of neoplastic Barrett's esophagus (BE), consisting in submucosal fluid injection and subsequent APC of visible BE. The aim of this study was to assess H-APC efficacy, safety, and tolerability.
Patients And Methods: We prospectively included patients undergoing H-APC ablation at four Italian Hospitals from September 2022 to March 2024.
YOLOSAMIC: A Hybrid Approach to Skin Cancer Segmentation with the Segment Anything Model and YOLOv8.
Diagnostics (Basel)
February 2025
Department of Dermatology, Sakarya University Training and Research Hospital, 54050 Serdivan, Türkiye.
: The rising global incidence of skin cancer emphasizes the urgent need for reliable and accurate diagnostic tools to aid early intervention. This study introduces YOLOSAMIC (YOLO and SAM in Cancer Imaging), a fully automated segmentation framework that integrates YOLOv8 for lesion detection, and the Segment Anything Model (SAM)-Box for precise segmentation. The objective is to develop a reliable segmentation system that handles complex skin lesion characteristics without requiring manual intervention.
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