Results 191 to 200 of about 7,181,199 (327)

Enhanced Pelvic CT Segmentation via Deep Learning: A Study on Loss Function Effects. [PDF]

J Imaging Inform Med
Ghaedi E, Asadi A, Hosseini SA, Arabi H.
europepmc   +1 more source

Raman spectroscopy-based prediction of ofloxacin concentration in solution using a novel loss function and an improved GA-CNN model. [PDF]

BMC Bioinformatics, 2023
Ma C, Shi Y, Huang Y, Dai G.
europepmc   +1 more source

Compound Heterozygous Complete Loss-of-Function <em>SPINK1</em> Variants as a Novel Cause of Severe Infantile Isolated Exocrine Pancreatic Insufficiency

Emmanuelle Masson, Marc Wangermez, David Tougeron, Vinciane Rebours, Claude Férec, Jian‐Min Chen   +5 more
openalex   +2 more sources

NKCC1: A key regulator of glioblastoma progression

Molecular Oncology, EarlyView.
Glioblastoma (GBM) progression is driven by disrupted chloride cotransporter homeostasis. NKCC1 is highly expressed in stem‐like, astrocytic, and progenitor cells, correlating with earlier recurrence, while overall survival remains unaffected. NKCC1 serves as a prognostic marker and potential therapeutic target, linking chloride transporter imbalance ...
Anja Thomsen, Diana Freitag, Madlen Haase, Christian Senft, Falko Schwarz, Silke Keiner   +5 more
wiley   +1 more source

Optimized UNet framework with a joint loss function for underwater image enhancement. [PDF]

Sci Rep
Wang X, Luo Z, Huang W, Zhang Y, Hu R.
europepmc   +1 more source

DRU-Net: Pulmonary Artery Segmentation via Dense Residual U-Network with Hybrid Loss Function. [PDF]

Sensors (Basel), 2023
Zulfiqar M, Stanuch M, Wodzinski M, Skalski A.   +3 more
europepmc   +1 more source

Rare loss-of-function variants in KMT2F are associated with schizophrenia and developmental disorders [PDF]

, 2016
Tarjinder Singh, Mitja Kurki, David Curtis, Shaun Purcell, Lucy Crooks, Jeremy F. McRae, Jaana Suvisaari, Himanshu Chheda, Douglas Blackwood, Gerome Breen, Olli Pietiläinen, Sebastian S. Gerety, Muhammad Ayub, Moira Blyth, Trevor Cole, David Collier, Eve L. Coomber, Nick Craddock, Mark J. Daly, John Danesh, Marta Di Forti, Alison Foster, Nelson B. Freimer, Daniel H. Geschwind, Mandy Johnstone, Shelagh Joss, George Kirov, Jarmo Körkkö, Outi Kuismin, Peter Holmans, Christina M. Hultman, Conrad Iyegbe, Jouko Lönnqvist, Minna Männikkö, Steve McCarroll, Peter McGuffin, Andrew M. McIntosh, Andrew McQuillin, Jukka S. Moilanen, Carmel Moore, Robin Murray, Ruth Newbury‐Ecob, Willem H. Ouwehand, Tiina Paunio, Elena Prigmore, Elliott Rees, David J. Roberts, Jennifer Sambrook, Pamela Sklar, David St Clair, Juha Veijola, James Walters, Hywel Williams, Swedish Schizophrenia Study, INTERVAL Study, DDD Study, Patrick F. Sullivan, Matthew E. Hurles, Michael O’Donovan, Aarno Palotie, Michael J. Owen, Jeffrey C. Barrett   +61 more
openalex   +1 more source

Overview of molecular signatures of senescence and associated resources: pros and cons

FEBS Open Bio, EarlyView.
Cells can enter a stress response state termed cellular senescence that is involved in various diseases and aging. Detecting these cells is challenging due to the lack of universal biomarkers. This review presents the current state of senescence identification, from biomarkers to molecular signatures, compares tools and approaches, and highlights ...
Orestis A. Ntintas, Sylvia Vagena, Pavlos Pantelis, Giorgos Theocharous, Russel Petty, Konstantinos Evangelou, Vassilis G. Gorgoulis   +6 more
wiley   +1 more source

CrackNet: A Hybrid Model for Crack Segmentation with Dynamic Loss Function. [PDF]

Sensors (Basel)
Fan Y, Hu Z, Li Q, Sun Y, Chen J, Zhou Q.   +5 more
europepmc   +1 more source

A Topological Loss Function for Deep-Learning Based Image Segmentation Using Persistent Homology. [PDF]

IEEE Trans Pattern Anal Mach Intell, 2022
Clough JR, Byrne N, Oksuz I, Zimmer VA, Schnabel JA, King AP.   +5 more
europepmc   +1 more source