2 citations · 5 across the 8 of their papers we have counts for
8 papers
When Tracking Fails: Analyzing Failure Modes of SAM2 for Point-Based Tracking in Surgical Videos
Woowon Jang, Jiwon Im, Juseung Choi +3
Video object segmentation (VOS) models such as SAM2 offer promising zero-shot tracking capabilities for surgical videos using minimal user input. Among the available input types, p…
Towards Improved Cervical Cancer Screening: Vision Transformer-Based Classification and Interpretability
Khoa Tuan Nguyen, Ho-min Park, Gaeun Oh +2
We propose a novel approach to cervical cell image classification for cervical cancer screening using the EVA-02 transformer model. We developed a four-step pipeline: fine-tuning E…
Boosting 3D Liver Shape Datasets with Diffusion Models and Implicit Neural Representations
Khoa Tuan Nguyen, Francesca Tozzi, Wouter Willaert +3
While the availability of open 3D medical shape datasets is increasing, offering substantial benefits to the research community, we have found that many of these datasets are, unfo…
Exploring Patient Data Requirements in Training Effective AI Models for MRI-based Breast Cancer Classification
Solha Kang, Wesley De Neve, Francois Rameau +1
The past decade has witnessed a substantial increase in the number of startups and companies offering AI-based solutions for clinical decision support in medical institutions. Howe…
Revisiting the Evaluation Bias Introduced by Frame Sampling Strategies in Surgical Video Segmentation Using SAM2
Utku Ozbulak, Seyed Amir Mousavi, Francesca Tozzi +4
Real-time video segmentation is a promising opportunity for AI-assisted surgery, offering intraoperative guidance by identifying tools and anatomical structures. Despite growing in…
Not All Adversarial Examples Require a Complex Defense: Identifying Over-optimized Adversarial Examples with IQR-based Logit Thresholding
Utku Ozbulak, Arnout Van Messem, Wesley De Neve
Detecting adversarial examples currently stands as one of the biggest challenges in the field of deep learning. Adversarial attacks, which produce adversarial examples, increase th…
How the Softmax Output is Misleading for Evaluating the Strength of Adversarial Examples
Utku Ozbulak, Wesley De Neve, Arnout Van Messem
Even before deep learning architectures became the de facto models for complex computer vision tasks, the softmax function was, given its elegant properties, already used to analyz…
Explaining Character-Aware Neural Networks for Word-Level Prediction: Do They Discover Linguistic Rules?
Fréderic Godin, Kris Demuynck, Joni Dambre +2
Character-level features are currently used in different neural network-based natural language processing algorithms. However, little is known about the character-level patterns th…