東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Trustworthy machine learning for hea...

TML4H (Workshop) (2023 :)

Linked to FindBook

Google Book

Amazon

博客來

Trustworthy machine learning for healthcare = first International Workshop, TML4H 2023, virtual event, May 4, 2023 : proceedings /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Trustworthy machine learning for healthcare/ edited by Hao Chen, Luyang Luo.
Reminder of title:	first International Workshop, TML4H 2023, virtual event, May 4, 2023 : proceedings /
remainder title:	TML4H 2023
other author:	Chen, Hao.
corporate name:	TML4H (Workshop)
Published:	Cham :Springer Nature Switzerland : : 2023.,
Description:	x, 198 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Do Tissue Source Sites leave identifiable Signatures in Whole Slide Images beyond staining? -- Explaining Multiclass Classifiers with Categorical Values: A Case Study in Radiography -- Privacy-preserving machine learning for healthcare: open challenges and future perspectives -- Self-Supervised Predictive Coding with Multimodal Fusion for Patient Deterioration Prediction in Fine-grained Time Resolution. Safe Exploration in Dose Finding Clinical Trials with Heterogeneous Participants. Isabel Chien, Javier Gonzalez Hernandez, Richard E Turner -- CGXplain: Rule-Based Deep Neural Network Explanations Using Dual Linear Programs -- ExBEHRT: Extended Transformer for Electronic Health Records -- Stasis: Reinforcement Learning Simulators for Human-Centric Real-World Environments. Cross-domain Microscopy Cell Counting by Disentangled Transfer Learning -- Post-hoc Saliency Methods Fail to Capture Latent Feature Importance in Time Series Data -- Enhancing Healthcare Model Trustworthiness through Theoretically Guaranteed One-Hidden-Layer CNN Purification -- A Kernel Density Estimation based Quality Metric for Quality Assessment of Obstetric Ultrasound Video -- Learn2Agree: Fitting with Multiple Annotators without Objective Ground Truth -- Conformal Prediction Masks: Visualizing Uncertainty in Medical Imaging -- Why Deep Surgical Models Fail?: Revisiting Surgical Action Triplet Recognition through the Lens of Robustness -- Geometry-Based end-to-end Segmentation of Coronary artery ib Computed Tomography Angiograph.
Contained By:	Springer Nature eBook
Subject:	Machine learning - Congresses. -
Online resource:	https://doi.org/10.1007/978-3-031-39539-0
ISBN:	9783031395390

Trustworthy machine learning for healthcare = first International Workshop, TML4H 2023, virtual event, May 4, 2023 : proceedings /
Trustworthy machine learning for healthcarefirst International Workshop, TML4H 2023, virtual event, May 4, 2023 : proceedings /[electronic resource] :TML4H 2023edited by Hao Chen, Luyang Luo. - Cham :Springer Nature Switzerland :2023. - x, 198 p. :ill. (some col.), digital ;24 cm. - Lecture notes in computer science,139320302-9743 ;. - Lecture notes in computer science ;13932..

Do Tissue Source Sites leave identifiable Signatures in Whole Slide Images beyond staining? -- Explaining Multiclass Classifiers with Categorical Values: A Case Study in Radiography -- Privacy-preserving machine learning for healthcare: open challenges and future perspectives -- Self-Supervised Predictive Coding with Multimodal Fusion for Patient Deterioration Prediction in Fine-grained Time Resolution. Safe Exploration in Dose Finding Clinical Trials with Heterogeneous Participants. Isabel Chien, Javier Gonzalez Hernandez, Richard E Turner -- CGXplain: Rule-Based Deep Neural Network Explanations Using Dual Linear Programs -- ExBEHRT: Extended Transformer for Electronic Health Records -- Stasis: Reinforcement Learning Simulators for Human-Centric Real-World Environments. Cross-domain Microscopy Cell Counting by Disentangled Transfer Learning -- Post-hoc Saliency Methods Fail to Capture Latent Feature Importance in Time Series Data -- Enhancing Healthcare Model Trustworthiness through Theoretically Guaranteed One-Hidden-Layer CNN Purification -- A Kernel Density Estimation based Quality Metric for Quality Assessment of Obstetric Ultrasound Video -- Learn2Agree: Fitting with Multiple Annotators without Objective Ground Truth -- Conformal Prediction Masks: Visualizing Uncertainty in Medical Imaging -- Why Deep Surgical Models Fail?: Revisiting Surgical Action Triplet Recognition through the Lens of Robustness -- Geometry-Based end-to-end Segmentation of Coronary artery ib Computed Tomography Angiograph.

This book constitutes the proceedings of First International Workshop, TML4H 2023, held virtually, in May 2023. The 16 full papers included in this volume were carefully reviewed and selected from 30 submissions. The goal of this workshop is to bring together experts from academia, clinic, and industry with an insightful vision of promoting trustworthy machine learning in healthcare in terms of scalability, accountability, and explainability.

ISBN: 9783031395390

Standard No.: 10.1007/978-3-031-39539-0doiSubjects--Topical Terms:

576368
Machine learning
--Congresses.

LC Class. No.: R859.7.M33

Dewey Class. No.: 610.285631

Trustworthy machine learning for healthcare = first International Workshop, TML4H 2023, virtual event, May 4, 2023 : proceedings /
LDR:03140nmm a2200349 a 4500 001 2332824
003 DE-He213
005 20230730010117.0
006 m d
007 cr nn 008maaau
008 240402s2023 sz s 0 eng d
020 $a 9783031395390 $q (electronic bk.)
020 $a 9783031395383 $q (paper)
024 7 $a 10.1007/978-3-031-39539-0 $2 doi
035 $a 978-3-031-39539-0
040 $a GP $c GP
041 0 $a eng
050 4 $a R859.7.M33
072 7 $a UYQM $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 610.285631 $2 23
090 $a R859.7.M33 $b T626 2023
111 2 $a TML4H (Workshop) $n (1st : $d 2023 : $c Online) $3 3663046
245 1 0 $a Trustworthy machine learning for healthcare $h [electronic resource] : $b first International Workshop, TML4H 2023, virtual event, May 4, 2023 : proceedings / $c edited by Hao Chen, Luyang Luo.
246 3 $a TML4H 2023
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2023.
300 $a x, 198 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Lecture notes in computer science, $x 0302-9743 ; $v 13932
505 0 $a Do Tissue Source Sites leave identifiable Signatures in Whole Slide Images beyond staining? -- Explaining Multiclass Classifiers with Categorical Values: A Case Study in Radiography -- Privacy-preserving machine learning for healthcare: open challenges and future perspectives -- Self-Supervised Predictive Coding with Multimodal Fusion for Patient Deterioration Prediction in Fine-grained Time Resolution. Safe Exploration in Dose Finding Clinical Trials with Heterogeneous Participants. Isabel Chien, Javier Gonzalez Hernandez, Richard E Turner -- CGXplain: Rule-Based Deep Neural Network Explanations Using Dual Linear Programs -- ExBEHRT: Extended Transformer for Electronic Health Records -- Stasis: Reinforcement Learning Simulators for Human-Centric Real-World Environments. Cross-domain Microscopy Cell Counting by Disentangled Transfer Learning -- Post-hoc Saliency Methods Fail to Capture Latent Feature Importance in Time Series Data -- Enhancing Healthcare Model Trustworthiness through Theoretically Guaranteed One-Hidden-Layer CNN Purification -- A Kernel Density Estimation based Quality Metric for Quality Assessment of Obstetric Ultrasound Video -- Learn2Agree: Fitting with Multiple Annotators without Objective Ground Truth -- Conformal Prediction Masks: Visualizing Uncertainty in Medical Imaging -- Why Deep Surgical Models Fail?: Revisiting Surgical Action Triplet Recognition through the Lens of Robustness -- Geometry-Based end-to-end Segmentation of Coronary artery ib Computed Tomography Angiograph.
520 $a This book constitutes the proceedings of First International Workshop, TML4H 2023, held virtually, in May 2023. The 16 full papers included in this volume were carefully reviewed and selected from 30 submissions. The goal of this workshop is to bring together experts from academia, clinic, and industry with an insightful vision of promoting trustworthy machine learning in healthcare in terms of scalability, accountability, and explainability.
650 0 $a Machine learning $x Congresses. $3 576368
650 0 $a Artificial intelligence $x Medical applications $x Congresses. $3 660945
650 1 4 $a Machine Learning. $3 3382522
700 1 $a Chen, Hao. $3 1935153
700 1 $a Luo, Luyang. $3 3663047
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Lecture notes in computer science ; $v 13932. $3 3663048
856 4 0 $u https://doi.org/10.1007/978-3-031-39539-0
950 $a Computer Science (SpringerNature-11645)