arXiv subjects

Andrei Buin

Publications and source records attributed to Andrei Buin.

3 recordsLinked to original sources

arXiv2024

Computational Pathology: A Survey Review and The Way Forward

Computational Pathology CPath is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath (https://github.com/AtlasAnalyticsLab/CPath_Survey).

eess.IV↗

arXiv2023

De-novo Chemical Reaction Generation by Means of Temporal Convolutional Neural Networks

We present here a combination of two networks, Recurrent Neural Networks (RNN) and Temporarily Convolutional Neural Networks (TCN) in de novo reaction generation using the novel Reaction Smiles-like representation of reactions (CGRSmiles) with atom mapping directly incorporated. Recurrent Neural Networks are known for their autoregressive properties and are frequently used in language modelling with direct application to SMILES generation. The relatively novel TCNs possess similar properties with wide receptive field while obeying the causality required for natural language processing (NLP). The combination of both latent representations expressed through TCN and RNN results in an overall better performance compared to RNN alone. Additionally, it is shown that different fine-tuning protocols have a profound impact on generative scope of the model when applied on a dataset of interest via transfer learning.

cs.LG↗

arXiv2017

Photo absorption enhancement in strained silicon nanowires: An atomistic study

The absorption spectra of silicon nanowires (SiNW) are calculated using semi-empirical $sp^{3}d^{5}s^{*}$ tight binding (TB) and Density Functional Theory (DFT) methods. The role of diameter, wave function symmetry, strain and crystallographic direction in determining the absorption are discussed. We find that compressive strain can change the band edge absorption by more than one order of magnitude due to change in wave function symmetry. In addition, photon polarization with respect to the nanowire axis significantly alters the band edge absorption. Overall, the band edge absorption of [110] and [100] silicon nanowires can differ by as much as three orders of magnitude. We find that compared to bulk Silicon, a strained Silicon nanowire array can absorb infrared photons (1.1 eV) approximately one hundred times better. Finally, we compare a fully numerical and a computationally efficient semi-analytical method, and find that they both yield satisfactory values of the band edge absorption.

cond-mat.mes-hall↗