2,251 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
Transformer Convolutional Neural Networks for Automated Artifact Detection in Scalp EEG
It is well known that electroencephalograms (EEGs) often contain artifacts
due to muscle activity, eye blinks, and various other causes. Detecting such
artifacts is an essential first step toward a correct interpretation of EEGs.
Although much effort has been devoted to semi-automated and automated artifact
detection in EEG, the problem of artifact detection remains challenging. In
this paper, we propose a convolutional neural network (CNN) enhanced by
transformers using belief matching (BM) loss for automated detection of five
types of artifacts: chewing, electrode pop, eye movement, muscle, and shiver.
Specifically, we apply these five detectors at individual EEG channels to
distinguish artifacts from background EEG. Next, for each of these five types
of artifacts, we combine the output of these channel-wise detectors to detect
artifacts in multi-channel EEG segments. These segment-level classifiers can
detect specific artifacts with a balanced accuracy (BAC) of 0.947, 0.735,
0.826, 0.857, and 0.655 for chewing, electrode pop, eye movement, muscle, and
shiver artifacts, respectively. Finally, we combine the outputs of the five
segment-level detectors to perform a combined binary classification (any
artifact vs. background). The resulting detector achieves a sensitivity (SEN)
of 60.4%, 51.8%, and 35.5%, at a specificity (SPE) of 95%, 97%, and 99%,
respectively. This artifact detection module can reject artifact segments while
only removing a small fraction of the background EEG, leading to a cleaner EEG
for further analysis.Comment: This is an extension to a paper presented at the 2022 44th Annual
International Conference of the IEEE Engineering in Medicine & Biology
Society (EMBC) Scottish Event Campus, Glasgow, UK, July 11-15, 202
An Empirical Study on the Language Modal in Visual Question Answering
Generalization beyond in-domain experience to out-of-distribution data is of
paramount significance in the AI domain. Of late, state-of-the-art Visual
Question Answering (VQA) models have shown impressive performance on in-domain
data, partially due to the language priors bias which, however, hinders the
generalization ability in practice. This paper attempts to provide new insights
into the influence of language modality on VQA performance from an empirical
study perspective. To achieve this, we conducted a series of experiments on six
models. The results of these experiments revealed that, 1) apart from prior
bias caused by question types, there is a notable influence of postfix-related
bias in inducing biases, and 2) training VQA models with word-sequence-related
variant questions demonstrated improved performance on the out-of-distribution
benchmark, and the LXMERT even achieved a 10-point gain without adopting any
debiasing methods. We delved into the underlying reasons behind these
experimental results and put forward some simple proposals to reduce the
models' dependency on language priors. The experimental results demonstrated
the effectiveness of our proposed method in improving performance on the
out-of-distribution benchmark, VQA-CPv2. We hope this study can inspire novel
insights for future research on designing bias-reduction approaches.Comment: Accepted by IJCAI202
Few-shot Multi-domain Knowledge Rearming for Context-aware Defence against Advanced Persistent Threats
Advanced persistent threats (APTs) have novel features such as multi-stage
penetration, highly-tailored intention, and evasive tactics. APTs defense
requires fusing multi-dimensional Cyber threat intelligence data to identify
attack intentions and conducts efficient knowledge discovery strategies by
data-driven machine learning to recognize entity relationships. However,
data-driven machine learning lacks generalization ability on fresh or unknown
samples, reducing the accuracy and practicality of the defense model. Besides,
the private deployment of these APT defense models on heterogeneous
environments and various network devices requires significant investment in
context awareness (such as known attack entities, continuous network states,
and current security strategies). In this paper, we propose a few-shot
multi-domain knowledge rearming (FMKR) scheme for context-aware defense against
APTs. By completing multiple small tasks that are generated from different
network domains with meta-learning, the FMKR firstly trains a model with good
discrimination and generalization ability for fresh and unknown APT attacks. In
each FMKR task, both threat intelligence and local entities are fused into the
support/query sets in meta-learning to identify possible attack stages.
Secondly, to rearm current security strategies, an finetuning-based deployment
mechanism is proposed to transfer learned knowledge into the student model,
while minimizing the defense cost. Compared to multiple model replacement
strategies, the FMKR provides a faster response to attack behaviors while
consuming less scheduling cost. Based on the feedback from multiple real users
of the Industrial Internet of Things (IIoT) over 2 months, we demonstrate that
the proposed scheme can improve the defense satisfaction rate.Comment: It has been accepted by IEEE SmartNet
STAGE: Span Tagging and Greedy Inference Scheme for Aspect Sentiment Triplet Extraction
Aspect Sentiment Triplet Extraction (ASTE) has become an emerging task in
sentiment analysis research, aiming to extract triplets of the aspect term, its
corresponding opinion term, and its associated sentiment polarity from a given
sentence. Recently, many neural networks based models with different tagging
schemes have been proposed, but almost all of them have their limitations:
heavily relying on 1) prior assumption that each word is only associated with a
single role (e.g., aspect term, or opinion term, etc. ) and 2) word-level
interactions and treating each opinion/aspect as a set of independent words.
Hence, they perform poorly on the complex ASTE task, such as a word associated
with multiple roles or an aspect/opinion term with multiple words. Hence, we
propose a novel approach, Span TAgging and Greedy infErence (STAGE), to extract
sentiment triplets in span-level, where each span may consist of multiple words
and play different roles simultaneously. To this end, this paper formulates the
ASTE task as a multi-class span classification problem. Specifically, STAGE
generates more accurate aspect sentiment triplet extractions via exploring
span-level information and constraints, which consists of two components,
namely, span tagging scheme and greedy inference strategy. The former tag all
possible candidate spans based on a newly-defined tagging set. The latter
retrieves the aspect/opinion term with the maximum length from the candidate
sentiment snippet to output sentiment triplets. Furthermore, we propose a
simple but effective model based on the STAGE, which outperforms the
state-of-the-arts by a large margin on four widely-used datasets. Moreover, our
STAGE can be easily generalized to other pair/triplet extraction tasks, which
also demonstrates the superiority of the proposed scheme STAGE.Comment: Accepted by AAAI 202
Deciphering of interactions between platinated DNA and HMGB1 by hydrogen/deuterium exchange mass spectrometry
A high mobility group box 1 (HMGB1) protein has been reported to recognize both 1,2-intrastrand crosslinked DNA by cisplatin (1,2-cis-Pt-DNA) and monofunctional platinated DNA using trans-[PtCl2(NH3)(thiazole)] (1-trans-PtTz-DNA). However, the molecular basis of recognition between the trans-PtTz-DNA and HMGB1 remains unclear. In the present work, we described a hydrogen/deuterium exchange mass spectrometry (HDX-MS) method in combination with docking simulation to decipher the interactions of platinated DNA with domain A of HMGB1. The global deuterium uptake results indicated that 1-trans-PtTz-DNA bound to HMGB1a slightly tighter than the 1,2-cis-Pt-DNA. The local deuterium uptake at the peptide level revealed that the helices I and II, and loop 1 of HMGB1a were involved in the interactions with both platinated DNA adducts. However, docking simulation disclosed different H-bonding networks and distinct DNA-backbone orientations in the two Pt-DNA-HMGB1a complexes. Moreover, the Phe37 residue of HMGB1a was shown to play a key role in the recognition between HMGB1a and the platinated DNAs. In the cis-Pt-DNA-HMGB1a complex, the phenyl ring of Phe37 intercalates into a hydrophobic notch created by the two platinated guanines, while in the trans-PtTz-DNA-HMGB1a complex the phenyl ring appears to intercalate into a hydrophobic crevice formed by the platinated guanine and the opposite adenine in the complementary strand, forming a penta-layer π–π stacking associated with the adjacent thymine and the thiazole ligand. This work demonstrates that HDX-MS associated with docking simulation is a powerful tool to elucidate the interactions between platinated DNAs and proteins
- …