A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
A deep learning approach classifies medical images hierarchically.
TSSC images enhance chaotic signal classification using ConvNets.
Improved self-supervised learning for document images.
Breast cancer has the highest mortality among cancers in women. Computer-aided pathology to analyze microscopic histopathology images for diagnosis with an increasing number of breast cancer patients can bring the cost and delays of diagnosis down. Deep learning in histopathology has attracted attention over the last d…
Enhances image classification by integrating semantic hierarchy into CNN models.
A DenseNet model classifies metastatic cancer in medical images.
Image classification requires the generation of features capable of detecting image patterns informative of group identity. The objective of this study was to classify images from the public CIFAR-10 image dataset by leveraging combinations of disparate image feature sources from both manual and deep learning approache…
This paper improves land cover classification using global spatial features in CNN.
JPEG2000 (j2k) is a highly popular format for image and video compression.With the rapidly growing applications of cloud based image classification, most existing j2k-compatible schemes would stream compressed color images from the source before reconstruction at the processing center as inputs to deep CNNs. We propose…
C2G-Net improves image classification of similar objects like cells.
Study examines how digital image alterations affect AI classification models.
New method uses contours of segmented images for X-ray classification.
Study improves pollen detection in optical and holographic images using deep learning.
The paper uses VGG-19 for plant species classification from leaf images.
There is growing interest in multi-label image classification due to its critical role in web-based image analytics-based applications, such as large-scale image retrieval and browsing. Matrix completion has recently been introduced as a method for transductive (semi-supervised) multi-label classification, and has seve…
Automated recognition and classification of bacteria species from microscopic images have significant importance in clinical microbiology. Bacteria classification is usually carried out manually by biologists using different shapes and morphologic characteristics of bacteria species. The manual taxonomy of bacteria typ…
A new method classifies color images using quaternion algebra.
SML improves pancreatic mass diagnosis accuracy using CT images.
New DAM method improves AUC scores in medical image classification.
Computational ghost imaging is an imaging technique in which an object is imaged from light collected using a single-pixel detector with no spatial resolution. Recently, ghost cytometry has been proposed for a high-speed cell-classification method that involves ghost imaging and machine learning in flow cytometry. Ghos…
Paper reduces neural network complexity for image classification.
Data augmentation is a widely used technique in many machine learning tasks, such as image classification, to virtually enlarge the training dataset size and avoid overfitting. Traditional data augmentation techniques for image classification tasks create new samples from the original training data by, for example, fli…
Sparse representations using overcomplete dictionaries have proved to be a powerful tool in many signal processing applications such as denoising, super-resolution, inpainting, compression or classification. The sparsity of the representation very much depends on how well the dictionary is adapted to the data at hand. …
BraidNet uses braid theory to optimize neural networks for image classification.
A new tensor network method for image classification reduces computation cost.
SaR-SVM-STV improves hyperspectral image classification with shape-adaptive reconstruction and denoising.
Active deep learning classification of hyperspectral images is considered in this paper. Deep learning has achieved success in many applications, but good-quality labeled samples are needed to construct a deep learning network. It is expensive getting good labeled samples in hyperspectral images for remote sensing appl…
Retail Product Image Classification is an important Computer Vision and Machine Learning problem for building real world systems like self-checkout stores and automated retail execution evaluation. In this work, we present various tricks to increase accuracy of Deep Learning models on different types of retail product …
End-to-end deep metric learning tackles multi-label image classification.
Image classification system identifies bumble bee species from images.
Novel fusion network combines polarization and radiomics features for liver cancer classification.
DeepBDC improves few-shot classification by measuring joint distributions of image features.
The thesis introduces methods to use semantic hierarchy in image classification.
AI enhances pollen recognition in veterinary imaging using holographic microscopy.
Cell detection and cell type classification from biomedical images play an important role for high-throughput imaging and various clinical application. While classification of single cell sample can be performed with standard computer vision and machine learning methods, analysis of multi-label samples (region containi…
The family of image visibility graphs (IVGs) have been recently introduced as simple algorithms by which scalar fields can be mapped into graphs. Here we explore the usefulness of such operator in the scenario of image processing and image classification. We demonstrate that the link architecture of the image visibilit…
Geographic object-based image analysis (GEOBIA) framework has gained increasing interest recently. Following this popular paradigm, we propose a novel multiscale classification approach operating on a hierarchical image representation built from two images at different resolutions. They capture the same scene with diff…
Enhances few-shot image classification using unlabelled examples.
A variety of real-world tasks involve the classification of images into pre-determined categories. Designing image classification algorithms that exhibit robustness to acquisition noise and image distortions, particularly when the available training data are insufficient to learn accurate models, is a significant chall…
This study investigates how much knowledge from natural images can be transferred to pathology images.
Tensor networks improve medical image classification performance.
Type 2 Diabetes (T2D) is a chronic metabolic disorder that can lead to blindness and cardiovascular disease. Information about early stage T2D might be present in retinal fundus images, but to what extent these images can be used for a screening setting is still unknown. In this study, deep neural networks were employe…
Paper uses CNN to predict stock price movement as an image classification problem.
The art of systematic financial trading evolved with an array of approaches, ranging from simple strategies to complex algorithms all relying, primary, on aspects of time-series analysis. Recently, after visiting the trading floor of a leading financial institution, we noticed that traders always execute their trade or…
Unified principle LZN unifies generative modeling, representation learning, and classification.
A method to monitor probability predictions for calibration loss in image classification models.
Defense against small image patches using occlusions.
The diagnosis, prognosis, and treatment of patients with musculoskeletal (MSK) disorders require radiology imaging (using computed tomography, magnetic resonance imaging(MRI), and ultrasound) and their precise analysis by expert radiologists. Radiology scans can also help assessment of metabolic health, aging, and diab…