A review of deep learning-based multiple-lesion recognition from medical images: classification, detection and segmentation

H Jiang, Z Diao, T Shi, Y Zhou, F Wang, W Hu… - Computers in Biology …, 2023 - Elsevier
Deep learning-based methods have become the dominant methodology in medical image
processing with the advancement of deep learning in natural image classification, detection …

Deep interactive segmentation of medical images: A systematic review and taxonomy

Z Marinov, PF Jäger, J Egger, J Kleesiek… - IEEE transactions on …, 2024 - ieeexplore.ieee.org
Interactive segmentation is a crucial research area in medical image analysis aiming to
boost the efficiency of costly annotations by incorporating human feedback. This feedback …

Rapid detection of mouse spermatogenic defects by testicular cellular composition analysis via enhanced deep learning model

N Ao, M Zang, Y Lu, Y Jiao, H Lu, C Cai, X Wang… - …, 2024 - Wiley Online Library
Background Histological analysis of the testicular sections is paramount in infertility research
but tedious and often requires months of training and practice. Objectives Establish an …

SATINN: An automated neural network-based classification of testicular sections allows for high-throughput histopathology of mouse mutants

R Yang, AM Stendahl, KA Vigh-Conrad, M Held… - …, 2022 - academic.oup.com
Motivation The mammalian testis is a complex organ with a cellular composition that
changes smoothly and cyclically in normal adults. While testis histology is already an …

Detection of spermatogonial stem/progenitor cells in prepubertal mouse testis with deep learning

B Kahveci, S Önen, F Akal, P Korkusuz - Journal of Assisted Reproduction …, 2023 - Springer
Purpose Rapid and easy detection of spermatogonial stem/progenitor cells (SSPCs) is
crucial for clinicians dealing with male infertility caused by prepubertal testicular damage …