Search Results for author:
Found 14 papers, 3 papers with code
HPL-ESS: Hybrid Pseudo-Labeling for Unsupervised Event-based Semantic Segmentation
In this paper, we propose a novel hybrid pseudo-labeling framework for unsupervised event-based semantic segmentation, HPL-ESS, to alleviate the influence of noisy pseudo labels.
Watermarking in Secure Federated Learning: A Verification Framework Based on Client-Side Backdooring
In this paper, we propose a novel client-side FL watermarking scheme to tackle the copyright protection issue in secure FL with HE.
MuSCLe: A Multi-Strategy Contrastive Learning Framework for Weakly Supervised Semantic Segmentation
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 annotations required by supervised semantic segmentation (SSS) methods.
Image Disentanglement Autoencoder for Steganography Without Embedding
Conventional steganography approaches embed a secret message into a carrier for concealed communication but are prone to attack by recent advanced steganalysis tools.
MCS-HMS: A Multi-Cluster Selection Strategy for the Human Mental Search Algorithm
This is not necessarily the best criterion to choose the winner group and limits the exploration ability of the algorithm.
HMS-OS: Improving the Human Mental Search Optimisation Algorithm by Grouping in both Search and Objective Space
The human mental search (HMS) algorithm is a relatively recent population-based metaheuristic algorithm, which has shown competitive performance in solving complex optimisation problems.
An Enhanced Differential Evolution Algorithm Using a Novel Clustering-based Mutation Operator
Differential evolution (DE) is an effective population-based metaheuristic algorithm for solving complex optimisation problems.
Automatic Foot Ulcer Segmentation Using an Ensemble of Convolutional Neural Networks
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.
Differential Evolution-based Neural Network Training Incorporating a Centroid-based Strategy and Dynamic Opposition-based Learning
This improves exploitation since the new member is obtained based on the best individuals, while the employed DOBL strategy, which uses the opposite of an individual, leads to enhanced exploration.
CryoNuSeg: A Dataset for Nuclei Instance Segmentation of Cryosectioned H&E-Stained Histological Images
Analysis of FS-derived H&E stained images can be more challenging as rapid preparation, staining, and scanning of FS sections may lead to deterioration in image quality.
Pollen Grain Microscopic Image Classification Using an Ensemble of Fine-Tuned Deep Convolutional Neural Networks
Our proposed method is shown to yield excellent classification performance, obtaining an accuracy of of 94. 48% and a weighted F1-score of 94. 54% on the ICPR 2020 Pollen Grain Classification Challenge training dataset based on five-fold cross-validation.
Investigating and Exploiting Image Resolution for Transfer Learning-based Skin Lesion Classification
Our results show that using very small images (of size 64x64 pixels) degrades the classification performance, while images of size 128x128 pixels and above support good performance with larger image sizes leading to slightly improved classification.
Skin Lesion Classification Using Hybrid Deep Neural Networks
In this work, we propose a fully automatic computerised method for skin lesion classification which employs optimised deep features from a number of well-established CNNs and from different abstraction levels.
Lesion Border Detection in Dermoscopy Images Using Ensembles of Thresholding Methods
Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions.
