425 citations · 841 across the 13 of their papers we have counts for
13 papers
MuSCLe: A Multi-Strategy Contrastive Learning Framework for Weakly Supervised Semantic Segmentation
Kunhao Yuan, Gerald Schaefer, Yu-Kun Lai +4
Weakly supervised semantic segmentation (WSSS) has gained significant popularity since it relies only on weak labels such as image level annotations rather than pixel level annotat…
MCS-HMS: A Multi-Cluster Selection Strategy for the Human Mental Search Algorithm
Ehsan Bojnordi, Seyed Jalaleddin Mousavirad, Gerald Schaefer +1
Population-based metaheuristic algorithms have received significant attention in global optimisation. Human Mental Search (HMS) is a relatively recent population-based metaheuristi…
An Enhanced Differential Evolution Algorithm Using a Novel Clustering-based Mutation Operator
Seyed Jalaleddin Mousavirad, Gerald Schaefer, Iakov Korovin +3
Differential evolution (DE) is an effective population-based metaheuristic algorithm for solving complex optimisation problems. However, the performance of DE is sensitive to the m…
Automatic Foot Ulcer Segmentation Using an Ensemble of Convolutional Neural Networks
Amirreza Mahbod, Gerald Schaefer, Rupert Ecker +1
Foot ulcer is a common complication of diabetes mellitus and, associated with substantial morbidity and mortality, remains a major risk factor for lower leg amputations. Extracting…
Differential Evolution-based Neural Network Training Incorporating a Centroid-based Strategy and Dynamic Opposition-based Learning
Seyed Jalaleddin Mousavirad, Diego Oliva, Salvador Hinojosa +1
Training multi-layer neural networks (MLNNs), a challenging task, involves finding appropriate weights and biases. MLNN training is important since the performance of MLNNs is main…
CryoNuSeg: A Dataset for Nuclei Instance Segmentation of Cryosectioned H&E-Stained Histological Images
Amirreza Mahbod, Gerald Schaefer, Benjamin Bancher +4
Nuclei instance segmentation plays an important role in the analysis of Hematoxylin and Eosin (H&E)-stained images. While supervised deep learning (DL)-based approaches represent t…