1,159 research outputs found
MProtoNet: A Case-Based Interpretable Model for Brain Tumor Classification with 3D Multi-parametric Magnetic Resonance Imaging
Recent applications of deep convolutional neural networks in medical imaging
raise concerns about their interpretability. While most explainable deep
learning applications use post hoc methods (such as GradCAM) to generate
feature attribution maps, there is a new type of case-based reasoning models,
namely ProtoPNet and its variants, which identify prototypes during training
and compare input image patches with those prototypes. We propose the first
medical prototype network (MProtoNet) to extend ProtoPNet to brain tumor
classification with 3D multi-parametric magnetic resonance imaging (mpMRI)
data. To address different requirements between 2D natural images and 3D mpMRIs
especially in terms of localizing attention regions, a new attention module
with soft masking and online-CAM loss is introduced. Soft masking helps sharpen
attention maps, while online-CAM loss directly utilizes image-level labels when
training the attention module. MProtoNet achieves statistically significant
improvements in interpretability metrics of both correctness and localization
coherence (with a best activation precision of ) without
human-annotated labels during training, when compared with GradCAM and several
ProtoPNet variants. The source code is available at
https://github.com/aywi/mprotonet.Comment: 15 pages, 5 figures, 1 table; accepted for oral presentation at MIDL
2023 (https://openreview.net/forum?id=6Wbj3QCo4U4); camera-ready versio
Risk and contributing factors of ecosystem shifts over naturally vegetated land under climate change in China.
Identifying the areas at risk of ecosystem transformation and the main contributing factors to the risk is essential to assist ecological adaptation to climate change. We assessed the risk of ecosystem shifts in China using the projections of four global gridded vegetation models (GGVMs) and an aggregate metric. The results show that half of naturally vegetated land surface could be under moderate or severe risk at the end of the 21st century under the middle and high emission scenarios. The areas with high risk are the Tibetan Plateau region and an area extended northeastward from the Tibetan Plateau to northeast China. With the three major factors considered, the change in carbon stocks is the main contributing factor to the high risk of ecosystem shifts. The change in carbon fluxes is another important contributing factor under the high emission scenario. The change in water fluxes is a less dominant factor except for the Tibetan Plateau region under the high emission scenario. Although there is considerable uncertainty in the risk assessment, the geographic patterns of the risk are generally consistent across different scenarios. The results could help develop regional strategies for ecosystem conservation to cope with climate change
Pulmonary alveolar type I cell population consists of two distinct subtypes that differ in cell fate.
Pulmonary alveolar type I (AT1) cells cover more than 95% of alveolar surface and are essential for the air-blood barrier function of lungs. AT1 cells have been shown to retain developmental plasticity during alveolar regeneration. However, the development and heterogeneity of AT1 cells remain largely unknown. Here, we conducted a single-cell RNA-seq analysis to characterize postnatal AT1 cell development and identified insulin-like growth factor-binding protein 2 (Igfbp2) as a genetic marker specifically expressed in postnatal AT1 cells. The portion of AT1 cells expressing Igfbp2 increases during alveologenesis and in post pneumonectomy (PNX) newly formed alveoli. We found that the adult AT1 cell population contains both Hopx+Igfbp2+ and Hopx+Igfbp2- AT1 cells, which have distinct cell fates during alveolar regeneration. Using an Igfbp2-CreER mouse model, we demonstrate that Hopx+Igfbp2+ AT1 cells represent terminally differentiated AT1 cells that are not able to transdifferentiate into AT2 cells during post-PNX alveolar regeneration. Our study provides tools and insights that will guide future investigations into the molecular and cellular mechanism or mechanisms underlying AT1 cell fate during lung development and regeneration
Low-redundancy codes for correcting multiple short-duplication and edit errors
Due to its higher data density, longevity, energy efficiency, and ease of
generating copies, DNA is considered a promising storage technology for
satisfying future needs. However, a diverse set of errors including deletions,
insertions, duplications, and substitutions may arise in DNA at different
stages of data storage and retrieval. The current paper constructs
error-correcting codes for simultaneously correcting short (tandem)
duplications and at most edits, where a short duplication generates a copy
of a substring with length and inserts the copy following the original
substring, and an edit is a substitution, deletion, or insertion. Compared to
the state-of-the-art codes for duplications only, the proposed codes correct up
to edits (in addition to duplications) at the additional cost of roughly
symbols of redundancy, thus achieving the same
asymptotic rate, where is the alphabet size and is a constant.
Furthermore, the time complexities of both the encoding and decoding processes
are polynomial when is a constant with respect to the code length.Comment: 21 pages. The paper has been submitted to IEEE Transaction on
Information Theory. Furthermore, the paper was presented in part at the
ISIT2021 and ISIT202
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