Manifold learning with arbitrary norms
Manifold learning methods play a prominent role in nonlinear dimensionality reduction and
other tasks involving high-dimensional data sets with low intrinsic dimensionality. Many of …
other tasks involving high-dimensional data sets with low intrinsic dimensionality. Many of …
PlayNet: real-time handball play classification with Kalman embeddings and neural networks
Real-time play recognition and classification algorithms are crucial for automating video
production and live broadcasts of sporting events. However, current methods relying on …
production and live broadcasts of sporting events. However, current methods relying on …
Cascaded Cross-Attention Networks for Data-Efficient Whole-Slide Image Classification Using Transformers
Abstract Whole-Slide Imaging allows for the capturing and digitization of high-resolution
images of histological specimen. An automated analysis of such images using deep …
images of histological specimen. An automated analysis of such images using deep …
Spatiotemporal Pooling on Appropriate Topological Maps Represented as Two-Dimensional Images for EEG Classification
T Fukushima, R Miyamoto - arXiv preprint arXiv:2403.04353, 2024 - arxiv.org
Motor imagery classification based on electroencephalography (EEG) signals is one of the
most important brain-computer interface applications, although it needs further improvement …
most important brain-computer interface applications, although it needs further improvement …
Enhancing anomaly detection in Melanoma diagnosis through Self-Supervised Training and lesion comparison
Melanoma, a highly aggressive form of skin cancer notorious for its rapid metastasis,
necessitates early detection to mitigate complex treatment requirements. While considerable …
necessitates early detection to mitigate complex treatment requirements. While considerable …
Unsupervised classification of congenital inner ear malformations using DeepDiffusion for latent space representation
The identification of congenital inner ear malformations is a challenging task even for
experienced clinicians. In this study, we present the first automated method for classifying …
experienced clinicians. In this study, we present the first automated method for classifying …
Comparison of attention models and post-hoc explanation methods for embryo stage identification: a case study
An important limitation to the development of AI-based solutions for In Vitro Fertilization (IVF)
is the black-box nature of most state-of-the-art models, due to the complexity of deep …
is the black-box nature of most state-of-the-art models, due to the complexity of deep …
Evaluating contextualized embeddings for topic modeling in public bidding domain
Public procurement plays a crucial role in government operations by acquiring goods and
services through competitive bidding processes. However, the increasing volume of …
services through competitive bidding processes. However, the increasing volume of …
Cluster analysis of low-dimensional medical concept representations from electronic health records
The study of existing links among different types of medical concepts can support research
on optimal pathways for the treatment of human diseases. Here, we present a clustering …
on optimal pathways for the treatment of human diseases. Here, we present a clustering …
Using Whole Slide Image Representations from Self-supervised Contrastive Learning for Melanoma Concordance Regression
S Grullon, V Spurrier, J Zhao, C Chivers… - … on Computer Vision, 2022 - Springer
Although melanoma occurs more rarely than several other skin cancers, patients' long term
survival rate is extremely low if the diagnosis is missed. Diagnosis is complicated by a high …
survival rate is extremely low if the diagnosis is missed. Diagnosis is complicated by a high …