Results 211 to 220 of about 76,480 (326)

Weakly supervised learning through box annotations for pig instance segmentation. [PDF]

Sci Rep
Zhou H, Dong J, Han S, Chung S, Ali H, Kim S.   +5 more
europepmc   +1 more source

Generative AI‐Driven Accelerated Discovery of Passivation Molecules for Perovskite Solar Cells

Advanced Science, EarlyView.
A generative artificial intelligence (AI) framework combining a discriminative machine learning model (SMILES‐X) and a generative language model (GPT‐2) autonomously discovers new molecular passivators for perovskite solar cells (PSCs). Through an iterative design loop, over 100 000 candidates are generated and screened, and randomly selected molecules
Adroit T. N. Fajar, Guillaume Lambard, Jessie Manopo, Ruili Guo, Kevin Septioga, Rizfi F. Pari, Toshinori Matsushima, Zhanglin Guo   +7 more
wiley   +1 more source

Weakly Supervised Learning on Large Graphs

Graph classification plays a pivotal role in various domains, including pathology, where images can be represented as graphs. In this domain, images can be represented as graphs, where nodes might represent individual nuclei, and edges capture the spatial or functional relationships between them.
openaire   +2 more sources

High performance with fewer labels using semi-weakly supervised learning for pulmonary embolism diagnosis. [PDF]

NPJ Digit Med
Hu Z, Lin HM, Mathur S, Moreland R, Witiw CD, Jimenez-Juan L, Callejas MF, Deva DP, Sejdić E, Colak E.   +9 more
europepmc   +1 more source

A Rational Optimization Approach for the Development of a Multiplexed Lateral Flow Immunoassay: Detection of Nonepithelial Ovarian Cancer Markers in Human Serum

Advanced Science, EarlyView.
This study outlines the developmental pipeline of a multiplexed nanozyme‐based lateral flow immunoassay for the purpose of ovarian germ cell tumor detection. It demonstrates the application of a design of experiments optimization approach for nanozyme probe conjugate development.
Aida Abdelwahed, Carl E. Eskildsen, Luca Panariello, André Shamsabadi, Christy J. Sadler, Edmund H. Wilkes, Federico Galvanin, Yuxi Cheng, Sara Carvalho, Srdjan Saso, Molly M. Stevens   +10 more
wiley   +1 more source

Weakly supervised learning in thymoma histopathology classification: an interpretable approach. [PDF]

Front Med (Lausanne)
Wang C, Du X, Yan X, Teng X, Wang X, Yang Z, Chang H, Fan Y, Ran C, Lian J, Li C, Li H, Cui L, Jiang Y.   +13 more
europepmc   +1 more source

Lung Lesion Localization of COVID-19 From Chest CT Image: A Novel Weakly Supervised Learning Method. [PDF]

IEEE J Biomed Health Inform, 2021
Yang Z, Zhao L, Wu S, Chen CY.
europepmc   +1 more source

INB3P: A Multi‐Modal and Interpretable Co‐Attention Framework Integrating Property‐Aware Explanations and Memory‐Bank Contrastive Fusion for Blood–Brain Barrier Penetrating Peptide Discovery

Advanced Science, EarlyView.
INB3P is a multimodal framework for blood–brain barrier‐penetrating peptide prediction under extreme data scarcity and class imbalance. By combining physicochemical‐guided augmentation, sequence–structure co‐attention, and imbalance‐aware optimization, it improves predictive performance and interpretability.
Jingwei Lv, Qianyang Wu, Jian Liu, Binlu Yang, Yuanhao Li, Junlin Xu, Yajie Meng, Leyi Wei, Zilong Zhang, Quan Zou, Xiulai Li, Feifei Cui   +11 more
wiley   +1 more source

Weakly supervised learning for visual recognition

, 2017
Cette thèse s'intéresse au problème de la classification d'images, où l'objectif est de prédire si une catégorie sémantique est présente dans l'image, à partir de son contenu visuel. Pour analyser des images de scènes complexes, il est important d'apprendre des représentations localisées.
openaire   +1 more source

Weakly-supervised deep learning models in computational pathology

EBioMedicine, 2022
Tanya N. Augustine
doaj   +1 more source