4 research outputs found
Developing diverse ensemble architectures for automatic brain tumor classification
No full textBrain tumors pose a serious threat in our modern society, with a clear increase in global cases each year. Therefore, developing robust solutions that could automatically and reliably detect brain tumors in their early stages is of utmost importance. In our paper, we revisit the problem of building performant ensembles for clinical usage by maximizing the diversity of the member models during the training procedure. We present an improved, more robust, extended version of our framework and propose solutions that could be integrated into a Computer-Aided Diagnosis system to accurately classify some of the most common types of brain tumors: meningioma, glioma, and pituitary tumors. We show that the new framework based on the histogram loss can be seen as a natural extension of the former approach, as it also calculates the inner products of the latent vectors produced by each member to measure similarity, but at the same time, it also makes it possible to capture more complex patterns. We also present several variants of our framework to incorporate member models with varying dimensional feature vectors and to cope with imbalanced datasets. We evaluate our solutions on a clinically tested dataset of 3,064 T1-weighted contrast-enhanced magnetic resonance images and show that they greatly outperform other state-of-the-art approaches and the base architectures as well, achieving over 92% accuracy, 92% macro and weighted precision, 91% macro and 92% weighted F1 score, and over 90% macro and 92% weighted sensitivity. © The Author(s) 2024.L
APACS23
No full textA collection of manually annotated, digitized Pap smear images for recognizing cervical cancer in patients
Pixel-wise segmentation of cells in digitized Pap smear images
No full textAbstract A simple and cheap way to recognize cervical cancer is using light microscopic analysis of Pap smear images. Training artificial intelligence-based systems becomes possible in this domain, e.g., to follow the European recommendation to screen negative smears to reduce false negative cases. The first step for such a process is segmenting the cells. A large and manually segmented dataset is required for this task, which can be used to train deep learning-based solutions. We describe a corresponding dataset with accurate manual segmentations for the enclosed cells. Altogether, the APACS23 (Annotated PAp smear images for Cell Segmentation 2023) dataset contains about 37 000 manually segmented cells and is separated into dedicated training and test parts, which could be used for an official benchmark of scientific investigations or a grand challenge
A Cloud-based Machine Learning Pipeline for the Efficient Extraction of Insights from Customer Reviews
No full textThe efficiency of natural language processing has improved dramatically with
the advent of machine learning models, particularly neural network-based
solutions. However, some tasks are still challenging, especially when
considering specific domains. In this paper, we present a cloud-based system
that can extract insights from customer reviews using machine learning methods
integrated into a pipeline. For topic modeling, our composite model uses
transformer-based neural networks designed for natural language processing,
vector embedding-based keyword extraction, and clustering. The elements of our
model have been integrated and further developed to meet better the
requirements of efficient information extraction, topic modeling of the
extracted information, and user needs. Furthermore, our system can achieve
better results than this task's existing topic modeling and keyword extraction
solutions. Our approach is validated and compared with other state-of-the-art
methods using publicly available datasets for benchmarking
