Results 81 to 90 of about 58,990 (298)

Semantic-Segmentation-Suite

open access: yes, 2020
Acknowledge: this is original code in the github page. The link is https://guides.github.com/activities/citable-code/.
openaire   +1 more source

Transferable Semi-Supervised Semantic Segmentation

open access: yesProceedings of the AAAI Conference on Artificial Intelligence, 2018
The performance of deep learning based semantic segmentation models heavily depends on sufficient data with careful annotations. However, even the largest public datasets only provide samples with pixel-level annotations for rather limited semantic categories.
Xiao, Huaxin   +4 more
openaire   +2 more sources

UniMR: A Plug‐and‐Play Framework of Automated Molecular Recognition for Scanning Tunneling Microscopy

open access: yesAdvanced Science, EarlyView.
UniMR, a training‐free framework for automated molecular recognition in STM images. By integrating adaptive feature selection with CLIP embeddings and Gaussian Mixture Modeling, UniMR achieves robust performance across diverse molecular systems and low‐resolution conditions.
Ziqiang Cao   +10 more
wiley   +1 more source

Accurate Identification of Protein Binding Sites for All Drug Modalities Using ALLSites

open access: yesAdvanced Science, EarlyView.
ALLSites is a unified sequence‐based framework for identifying proteome‐wide binding sites across all drug modalities. It integrates a gated convolutional network with a transformer architecture to capture residue interactions directly from the sequence.
Minjie Mou   +14 more
wiley   +1 more source

CACLENS: A Multitask Deep Learning System for Enzyme Discovery

open access: yesAdvanced Science, EarlyView.
CACLENS, a multimodal and multi‐task deep learning framework integrating cross‐attention, contrastive learning, and customized gate control, enables reaction type classification, EC number prediction, and reaction feasibility assessment. CACLENS accelerates functional enzyme discovery and identifies efficient Zearalenone (ZEN)‐degrading enzymes.
Xilong Yi   +5 more
wiley   +1 more source

Inferring Gene Regulatory Networks From Single‐Cell RNA Sequencing Data by Dual‐Role Graph Contrastive Learning

open access: yesAdvanced Science, EarlyView.
RegGAIN is a novel and powerful deep learning framework for inferring gene regulatory networks (GRNs) from single‐cell RNA sequencing data. By integrating self‐supervised contrastive learning with dual‐role gene representations, it consistently outperforms existing methods in both accuracy and robustness.
Qiyuan Guan   +9 more
wiley   +1 more source

Biomechanics‐Driven 3D Architecture Inference from Histology Using CellSqueeze3D

open access: yesAdvanced Science, EarlyView.
CellSqueeze3D reconstructs 3D cellular architecture from standard 2D histology images using biomechanical constraints and optimization. Validated on clinical datasets, it enables accurate tissue phenotyping, predicts gene mutations, and reveals significant correlations between nuclear‐cytoplasmic ratio entropy and tumor progression.
Yan Kong, Hui Lu
wiley   +1 more source

MicrobeDiscover: A Knowledge Graph–Enabled AI Framework for Identifying Microbes for Inorganic Nanomaterial Biosynthesis

open access: yesAdvanced Science, EarlyView.
Microbial synthesis of nanomaterials (NMs) is eco‐friendly, but the screening of microorganisms is limited by inefficient traditional methods (currently only involving∽400 microorganisms/90 NMs). We propose AI framework MicrobeDiscover, integrating a knowledge graph of microbe‐NM interactions.
Ludi Wang   +12 more
wiley   +1 more source

TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation [PDF]

open access: green, 2021
Zhaoyuan Yin   +6 more
openalex   +1 more source

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