24 research outputs found
Lesion Search with Self-supervised Learning
Content-based image retrieval (CBIR) with self-supervised learning (SSL)
accelerates clinicians' interpretation of similar images without manual
annotations. We develop a CBIR from the contrastive learning SimCLR and
incorporate a generalized-mean (GeM) pooling followed by L2 normalization to
classify lesion types and retrieve similar images before clinicians' analysis.
Results have shown improved performance. We additionally build an open-source
application for image analysis and retrieval. The application is easy to
integrate, relieving manual efforts and suggesting the potential to support
clinicians' everyday activities.Comment: ICLR 2023 Tiny Pape
Guided Proofreading of Automatic Segmentations for Connectomics
Automatic cell image segmentation methods in connectomics produce merge and
split errors, which require correction through proofreading. Previous research
has identified the visual search for these errors as the bottleneck in
interactive proofreading. To aid error correction, we develop two classifiers
that automatically recommend candidate merges and splits to the user. These
classifiers use a convolutional neural network (CNN) that has been trained with
errors in automatic segmentations against expert-labeled ground truth. Our
classifiers detect potentially-erroneous regions by considering a large context
region around a segmentation boundary. Corrections can then be performed by a
user with yes/no decisions, which reduces variation of information 7.5x faster
than previous proofreading methods. We also present a fully-automatic mode that
uses a probability threshold to make merge/split decisions. Extensive
experiments using the automatic approach and comparing performance of novice
and expert users demonstrate that our method performs favorably against
state-of-the-art proofreading methods on different connectomics datasets.Comment: Supplemental material available at
http://rhoana.org/guidedproofreading/supplemental.pd
Promoting Sustainability through Next-Generation Biologics Drug Development
The fourth industrial revolution in 2011 aimed to transform the traditional manufacturing processes. As part of this revolution, disruptive innovations in drug development and data science approaches have the potential to optimize CMC (chemistry, manufacture, and control). The real-time simulation of processes using âdigital twinsâ can maximize efficiency while improving sustainability. As part of this review, we investigate how the World Health Organizationâs 17 sustainability goals can apply toward next-generation drug development. We analyze the state-of-the-art laboratory leadership, inclusive personnel recruiting, the latest therapy approaches, and intelligent process automation. We also outline how modern data science techniques and machine tools for CMC help to shorten drug development time, reduce failure rates, and minimize resource usage. Finally, we systematically analyze and compare existing approaches to our experiences with the high-throughput laboratory KIWI-biolab at the TU Berlin. We describe a sustainable business model that accelerates scientific innovations and supports global action toward a sustainable future.BMBF, 01DD20002A, Verbundprojekt: Internationales Zukunftslabor fĂŒr KI-gestĂŒtzte Bioprozessentwicklung "KIWI-biolab"; Teilvorhaben: Koordination und Aufbau eines KI-Exzellenzzentrum
SlicerTMS: Interactive Real-time Visualization of Transcranial Magnetic Stimulation using Augmented Reality and Deep Learning
Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation
approach that effectively treats various brain disorders. One of the critical
factors in the success of TMS treatment is accurate coil placement, which can
be challenging, especially when targeting specific brain areas for individual
patients. Calculating the optimal coil placement and the resulting electric
field on the brain surface can be expensive and time-consuming. We introduce
SlicerTMS, a simulation method that allows the real-time visualization of the
TMS electromagnetic field within the medical imaging platform 3D Slicer. Our
software leverages a 3D deep neural network, supports cloud-based inference,
and includes augmented reality visualization using WebXR. We evaluate the
performance of SlicerTMS with multiple hardware configurations and compare it
against the existing TMS visualization application SimNIBS. All our code, data,
and experiments are openly available:
\url{https://github.com/lorifranke/SlicerTMS}Comment: 11 pages, 3 figures, 2 tables, MICCA
Quantifying the Sampling Regime for CNNsâ Graphical Perception
The purpose of this study is to provide a framework for quantifying CNN decisions, specifically understanding different training input data preparation methods that influence prediction accuracy