arXiv research

New CNN approach detects cell nuclei with prior information.

Detection of cell nuclei in microscopic images is a challenging research topic, because of limitations in cellular image quality and diversity of nuclear morphology, i.e. varying nuclei shapes, sizes, and overlaps between multiple cell nuclei. This has been a topic of enduring interest with promising recent success sho…

An algorithm reduces breast cancer detection data complexity using effect sizes.

Accurate and robust cell nuclei classification is the cornerstone for a wider range of tasks in digital and Computational Pathology. However, most machine learning systems require extensive labeling from expert pathologists for each individual problem at hand, with no or limited abilities for knowledge transfer between…

Deep object detection improves mitotic nucleus detection in breast cancer biopsies.

Efficient and precise classification of histological cell nuclei is of utmost importance due to its potential applications in the field of medical image analysis. It would facilitate the medical practitioners to better understand and explore various factors for cancer treatment. The classification of histological cell …

A deep learning framework separates overlapping nuclei in histology images.

Proposes a new network to improve nuclei segmentation in histopathology images.

We introduce a new generalization of Gompf nuclei and give applications. We construct infinitely many exotic smooth structures for a large class of compact 4-manifolds with boundary, regarding topological invariants. We prove that a large class of closed 3-manifolds (including disjoint unions of Stein fillable 3-manifo…

A deep learning framework segments deep cerebellar nuclei from 7T MRI, improving accuracy and consistency.

Fast, accurate thalamus segmentation method for MS and ET.

In the recent years, Riemannian shape analysis of curves and surfaces has found several applications in medical image analysis. In this paper we present a numerical discretization of second order Sobolev metrics on the space of regular curves in Euclidean space. This class of metrics has several desirable mathematical …

Framework detects and classifies multi-label RBC images from microscopic images.

RVAE detects and repairs corrupted cells in mixed-type tabular data.

CST-YOLO improves blood cell detection with YOLOv7 and CNN-Swin Transformer.

Generative Distribution Embeddings learn multiscale representations of distributions.

Neural network improves breast cancer diagnosis with high accuracy.

NuClick uses clicks inside nuclei to improve nuclear segmentation.

This paper proposes synthetic augmentation for nuclei image segmentation in medical pathology.

Input-cell attention improves RNN saliency detection over time.

In the area of traditional physics the atomic nucleus belongs to the most complex systems. It involves essentially all elements that characterize complexity including the most distinctive one whose essence is a permanent coexistence of coherent patterns and of randomness. From a more interdisciplinary perspective, thes…

Statistical shape analysis can be done in a Riemannian framework by endowing the set of shapes with a Riemannian metric. Sobolev metrics of order two and higher on shape spaces of parametrized or unparametrized curves have several desirable properties not present in lower order metrics, but their discretization is stil…

Lung cancer continues to be a major healthcare challenge with high morbidity and mortality rates among both men and women worldwide. The majority of lung cancer cases are of non-small cell lung cancer type. With the advent of targeted cancer therapy, it is imperative not only to properly diagnose but also sub-classify …

Study of SK-N-AS cells' response to methamidophos using transcriptomics.

Based on the work of Durhuus-J{ó}nsson and Benedetti-Ziegler, we revisit the question of the number of triangulations of the 3-ball. We introduce a notion of nucleus (a triangulation of the 3-ball without internal nodes, and with each internal face having at most 1 external edge). We show that every triangulation can b…

NucleusDiff models atomic nuclei interactions to prevent separation violations in drug design.

Set classification problems arise when classification tasks are based on sets of observations as opposed to individual observations. In set classification, a classification rule is trained with $N$ sets of observations, where each set is labeled with class information, and the prediction of a class label is performed a…

Energy-efficient detection of natural errors in deep networks.

New methods detect continuous variation in single-cell data.

Model detects patterns in noisy binary data, explaining neuron activity in terms of cell assemblies.

Fink AGN classifier achieves high accuracy in classifying active galactic nuclei.

MINN-SA enhances cancer detection using TCR sequences with better interpretability.

We study the problem of instance segmentation in biological images with crowded and compact cells. We formulate this task as an integer program where variables correspond to cells and constraints enforce that cells do not overlap. To solve this integer program, we propose a column generation formulation where the prici…

This study automates blood cell classification using computer vision.

Deep neural network improves malaria detection from red blood cells.

A novel method detects multiple mitosis events and mitigates annotation gaps in phase-contrast microscopy.

Mogami introduced in 1995 a large class of triangulated 3-dimensional pseudomanifolds, henceforth called "Mogami pseudomanifolds". He proved an exponential bound for the size of this class in terms of the number of tetrahedra. The question of whether all 3-balls are Mogami has remained open since, a positive answer wou…

Study uses deep learning to detect BCCs in high-res histopathological images.

Proposes RCVs for explaining deep neural network predictions in medical images.

In this note we show that there are 4-manifolds not containing Gompf nucleus $N_2$; in this way we answer Problem 4.98 of Kirby's problem list in the negative.

We propose a probabilistic model for interpreting gene expression levels that are observed through single-cell RNA sequencing. In the model, each cell has a low-dimensional latent representation. Additional latent variables account for technical effects that may erroneously set some observations of gene expression leve…

Neuronal circuits formed in the brain are complex with intricate connection patterns. Such complexity is also observed in the retina as a relatively simple neuronal circuit. A retinal ganglion cell receives excitatory inputs from neurons in previous layers as driving forces to fire spikes. Analytical methods are requir…

The above named paper has been withdrawn. A colleague has observed a gap in the proof of isotopy invariance, which can be repaired by reducing the coefficients (which lie in (1/6)Z) of the antisymmetric kanji with chords incident with more than one component modulo 8Z. An analogous issue arises in considering the effec…

We consider few-body bound state systems and provide precise definitions of Borromean and Brunnian systems. The initial concepts are more than a hundred years old and originated in mathematical knot-theory as purely geometric considerations. About thirty years ago they were generalized and applied to the binding of sys…

TransST improves spatial transcriptomics data analysis by identifying cell clusters and biomarkers.

Bayesian analysis predicts properties of proton-emitting nuclei beyond the proton drip line.

A fundamental operation in many vision tasks, including motion understanding, stereopsis, visual odometry, or invariant recognition, is establishing correspondences between images or between images and data from other modalities. We present an analysis of the role that multiplicative interactions play in learning such …

Dynamic cell-free networks reduce complexity in serving many devices with distributed APs and DRL.