activity
20192026
collaborators

6 papers

cs.CY2026

Traceable Trust for action-ready artificial intelligence in bioscience

Huayu Xin, Yizhi Cai, Mukilan Deivarajan Suresh +9

Artificial intelligence (AI) is becoming part of the working infrastructure of the biosciences. AI models can predict biomolecular structures, design proteins, rank variants, annot…

cs.CV2025

Graph-Attention Network with Adversarial Domain Alignment for Robust Cross-Domain Facial Expression Recognition

Razieh Ghaedi, AmirReza BabaAhmadi, Reyer Zwiggelaar +2

Cross-domain facial expression recognition (CD-FER) remains difficult due to severe domain shift between training and deployment data. We propose Graph-Attention Network with Adver…

cs.CV2025

MACMD: Multi-dilated Contextual Attention and Channel Mixer Decoding for Medical Image Segmentation

Lalit Maurya, Honghai Liu, Reyer Zwiggelaar

Medical image segmentation faces challenges due to variations in anatomical structures. While convolutional neural networks (CNNs) effectively capture local features, they struggle…

cs.CV2025

TCSA-UDA: Text-Driven Cross-Semantic Alignment for Unsupervised Domain Adaptation in Medical Image Segmentation

Lalit Maurya, Honghai Liu, Reyer Zwiggelaar

Unsupervised domain adaptation (UDA) for medical image segmentation remains challenging due to substantial domain shifts across imaging modalities, such as CT and MRI. Although rec…

q-bio.BM2021

SHREC 2021: Retrieval and classification of protein surfaces equipped with physical and chemical properties

Andrea Raffo, Ulderico Fugacci, Silvia Biasotti +21

This paper presents the methods that have participated in the SHREC 2021 contest on retrieval and classification of protein surfaces on the basis of their geometry and physicochemi…

eess.IV2019

Automated Mammogram Analysis with a Deep Learning Pipeline

Azam Hamidinekoo, Erika Denton, Reyer Zwiggelaar

Current deep learning based detection models tackle detection and segmentation tasks by casting them to pixel or patch-wise classification. To automate the initial mass lesion dete…