Zobrazeno 1 - 10
of 161 438
pro vyhledávání: '"multi‐class"'
For anomaly detection (AD), early approaches often train separate models for individual classes, yielding high performance but posing challenges in scalability and resource management. Recent efforts have shifted toward training a single model capabl
Externí odkaz:
http://arxiv.org/abs/2412.04769
Pathological cell semantic segmentation is a fundamental technology in computational pathology, essential for applications like cancer diagnosis and effective treatment. Given that multiple cell types exist across various organs, with subtle differen
Externí odkaz:
http://arxiv.org/abs/2412.02978
Autor:
Zakir, Hussni Mohd, Ho, Eric Tatt Wei
The Segment-Anything Model (SAM) is a vision foundation model for segmentation with a prompt-driven framework. SAM generates class-agnostic masks based on user-specified instance-referring prompts. However, adapting SAM for automated segmentation --
Externí odkaz:
http://arxiv.org/abs/2411.13774
Despite significant advancements, segmentation based on deep neural networks in medical and surgical imaging faces several challenges, two of which we aim to address in this work. First, acquiring complete pixel-level segmentation labels for medical
Externí odkaz:
http://arxiv.org/abs/2411.09553
Autonomous driving necessitates advanced object detection techniques that integrate information from multiple modalities to overcome the limitations associated with single-modal approaches. The challenges of aligning diverse data in early fusion and
Externí odkaz:
http://arxiv.org/abs/2410.08739
Autor:
Nobari, Arash Dargahi, Rafiei, Davood
The integration of tabular data from diverse sources is often hindered by inconsistencies in formatting and representation, posing significant challenges for data analysts and personal digital assistants. Existing methods for automating tabular data
Externí odkaz:
http://arxiv.org/abs/2411.17110
Recent advancements in anomaly detection have shifted focus towards Multi-class Unified Anomaly Detection (MUAD), offering more scalable and practical alternatives compared to traditional one-class-one-model approaches. However, existing MUAD methods
Externí odkaz:
http://arxiv.org/abs/2411.16049
In this research, we explored the improvement in terms of multi-class disease classification via pre-trained language models over Medical-Abstracts-TC-Corpus that spans five medical conditions. We excluded non-cancer conditions and examined four spec
Externí odkaz:
http://arxiv.org/abs/2411.12712
Autor:
Yan, Yang, Chen, Zhong, Xu, Cai, Shen, Xinglei, Shiao, Jay, Einck, John, Chen, Ronald C, Gao, Hao
Patient-reported outcomes (PROs) directly collected from cancer patients being treated with radiation therapy play a vital role in assisting clinicians in counseling patients regarding likely toxicities. Precise prediction and evaluation of symptoms
Externí odkaz:
http://arxiv.org/abs/2411.10819
Autor:
Agarwal, Ishita, Patti, Taylor L., Bravo, Rodrigo Araiza, Yelin, Susanne F., Anandkumar, Anima
Quantum Neuromorphic Computing (QNC) merges quantum computation with neural computation to create scalable, noise-resilient algorithms for quantum machine learning (QML). At the core of QNC is the quantum perceptron (QP), which leverages the analog d
Externí odkaz:
http://arxiv.org/abs/2411.09093