Results 101 to 110 of about 31,109 (263)

Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics

Advanced Intelligent Discovery, EarlyView.
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong, Zijian Huang, Xuanwu Wang, Zhenghui Feng, Wei Liu, Bingding Huang   +5 more
wiley   +1 more source

RAN4IQA: Restorative Adversarial Nets for No-Reference Image Quality Assessment

, 2017
Inspired by the free-energy brain theory, which implies that human visual system (HVS) tends to reduce uncertainty and restore perceptual details upon seeing a distorted image, we propose restorative adversarial net (RAN), a GAN-based model for no ...
Chen, Diqi, Ren, Hongyu, Wang, Yizhou
core   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Patch is enough: naturalistic adversarial patch against vision-language pre-training models

Visual Intelligence
Visual language pre-training (VLP) models have demonstrated significant success in various domains, but they remain vulnerable to adversarial attacks. Addressing these adversarial vulnerabilities is crucial for enhancing security in multi-modal learning.
Dehong Kong, Siyuan Liang, Xiaopeng Zhu, Yuansheng Zhong, Wenqi Ren   +4 more
doaj   +1 more source

“Just a Patch”: Imperceptible Image Patch Generation for Adversarial Inference

IEEE Access
Image classification models, based on deep neural networks, are vulnerable to adversarial input poisoning attacks where a maliciously crafted input results in incorrect predictions.
Debasmita Manna, Somanath Tripathy, Bruhadeshwar Bezawada, Indrakshi Ray   +3 more
doaj   +1 more source

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

Advanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley   +1 more source

Uncertainty‐Guided Selective Adaptation Enables Cross‐Platform Predictive Fluorescence Microscopy

Advanced Intelligent Discovery, EarlyView.
Deep learning models often fail when transferred to new microscopes. A novel framework overcomes this by selectively adapting the early layers governing low‐level image statistics, while freezing deep layers that encode morphology. This uncertainty‐guided approach enables robust, label‐free virtual staining across diverse systems, democratizing ...
Kai‐Wen K. Yang, Andrew Bai, Alexandra Bermudez, Yunqi Hong, Zoe Latham, Iris Sloan, Michael Liu, Vishrut Goyal, Cho‐Jui Hsieh, Neil Y. C. Lin   +9 more
wiley   +1 more source

GAN‐LSTM‐3D: An efficient method for lung tumour 3D reconstruction enhanced by attention‐based LSTM

CAAI Transactions on Intelligence Technology, EarlyView., 2023
Abstract Three‐dimensional (3D) image reconstruction of tumours can visualise their structures with precision and high resolution. In this article, GAN‐LSTM‐3D method is proposed for 3D reconstruction of lung cancer tumours from 2D CT images. Our method consists of three phases: lung segmentation, tumour segmentation, and tumour 3D reconstruction. Lung
Lu Hong, Mohammad Hossein Modirrousta, Mohammad Hossein Nasirpour, Mohammadreza Mirshekari Chargari, Fardin Mohammadi, Seyed Vahid Moravvej, Leila Rezvanishad, Mohammadreza Rezvanishad, Ivan Bakhshayeshi, Roohallah Alizadehsani, Imran Razzak, Hamid Alinejad‐Rokny, Saeid Nahavandi   +12 more
wiley   +1 more source

Block-level masking and feature importance-based adversarial example generation

Journal of Big Data
This paper proposes a method to enhance the transferability of adversarial examples by combining a Learnable Patch-Wise Mask (LPM) generated through differential evolution algorithm with a Feature Importance Aware (FIA) attack.
Wenbo Qiu, Yafei Song
doaj   +1 more source

SAAM: Stealthy Adversarial Attack on Monocular Depth Estimation

IEEE Access
Monocular depth estimation (MDE) is an important task in scene understanding, and significant improvements in its performance have been witnessed with the utilization of convolutional neural networks (CNNs).
Amira Guesmi, Muhammad Abdullah Hanif, Bassem Ouni, Muhammad Shafique   +3 more
doaj   +1 more source