001454453 000__ 03709cam\a2200589\i\4500
001454453 001__ 1454453
001454453 003__ OCoLC
001454453 005__ 20230314003520.0
001454453 006__ m\\\\\o\\d\\\\\\\\
001454453 007__ cr\cn\nnnunnun
001454453 008__ 230208s2023\\\\sz\a\\\\o\\\\\101\0\eng\d
001454453 019__ $$a1368402715
001454453 020__ $$a9783031250460$$q(electronic bk.)
001454453 020__ $$a303125046X$$q(electronic bk.)
001454453 020__ $$z9783031250453
001454453 020__ $$z3031250451
001454453 0247_ $$a10.1007/978-3-031-25046-0$$2doi
001454453 035__ $$aSP(OCoLC)1369157530
001454453 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dEBLCP
001454453 049__ $$aISEA
001454453 050_4 $$aR857.O6
001454453 08204 $$a616.07/54$$223/eng/20230208
001454453 1112_ $$aMICCAI Workshop on Medical Applications with Disentanglements$$n(1st :$$d2022 :$$cSingapore).
001454453 24510 $$aMedical applications with disentanglements :$$bFirst MICCAI Workshop, MAD 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings /$$cJana Fragemann [and five others] (eds.).
001454453 24630 $$aMAD 2022
001454453 264_1 $$aCham :$$bSpringer,$$c[2023]
001454453 264_4 $$c©2023
001454453 300__ $$a1 online resource (x, 127 pages) :$$billustrations (some color).
001454453 336__ $$atext$$btxt$$2rdacontent
001454453 337__ $$acomputer$$bc$$2rdamedia
001454453 338__ $$aonline resource$$bcr$$2rdacarrier
001454453 4901_ $$aLecture notes in computer science ;$$v13823
001454453 500__ $$aInternational conference proceedings.
001454453 500__ $$aIncludes author index.
001454453 5050_ $$aApplying Disentanglement in the Medical Domain: An Introduction -- HSIC-InfoGAN: Learning Unsupervised Disentangled Representations by Maximising Approximated Mutual Information -- Implicit Embeddings via GAN Inversion for High Resolution Chest Radiographs -- Disentangled Representation Learning for Privacy-Preserving Case-Based Explanations -- Instance-Specific Augmentation of Brain MRIs with Variational Autoencoder -- Low-rank and Sparse Metamorphic Autoencoders for Unsupervised Pathology Disentanglement -- Training [beta]-VAE by Aggregating a Learned Gaussian Posterior with a Decoupled Decoder -- Disentangling Factors of Morpholigical Variation in an Invertible Brain Aging Model -- A study of representational properties of unsupervised anomaly detection in brain MRI.
001454453 506__ $$aAccess limited to authorized users.
001454453 520__ $$aThis book constitutes the post-conference proceedings of the First MICCAI Workshop on Medical Applications with Disentanglements, MAD 2022, held in conjunction with MICCAI 2022, in Singapore, on September22, 2022. The 8 full papers presented in this book together with one short paper were carefully reviewed and cover generative adversarial networks (GAN), variational autoencoders (VAE) and normalizing-flow architectures as well as a wide range of medical applications, like brain age prediction, skull reconstruction and unsupervised pathology disentanglement.
001454453 588__ $$aDescription based on print version record.
001454453 650_0 $$aImaging systems in medicine$$vCongresses.
001454453 650_0 $$aDiagnostic imaging$$vCongresses.
001454453 655_0 $$aElectronic books.
001454453 655_7 $$aConference papers and proceedings.$$2lcgft
001454453 7001_ $$aFragemann, Jana,$$eeditor.
001454453 7112_ $$aInternational Conference on Medical Image Computing and Computer-Assisted Intervention$$d(2022 :$$cSingapore)
001454453 77608 $$iPrint version:$$tMedical applications with disentanglements.$$dCham : Springer Nature Switzerland, 2023$$z9783031250453$$w(OCoLC)1363817261
001454453 830_0 $$aLecture notes in computer science ;$$v13823.
001454453 852__ $$bebk
001454453 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-25046-0$$zOnline Access$$91397441.1
001454453 909CO $$ooai:library.usi.edu:1454453$$pGLOBAL_SET
001454453 980__ $$aBIB
001454453 980__ $$aEBOOK
001454453 982__ $$aEbook
001454453 983__ $$aOnline
001454453 994__ $$a92$$bISE