54 research outputs found
Efficient video indexing for monitoring disease activity and progression in the upper gastrointestinal tract
Endoscopy is a routine imaging technique used for both diagnosis and
minimally invasive surgical treatment. While the endoscopy video contains a
wealth of information, tools to capture this information for the purpose of
clinical reporting are rather poor. In date, endoscopists do not have any
access to tools that enable them to browse the video data in an efficient and
user friendly manner. Fast and reliable video retrieval methods could for
example, allow them to review data from previous exams and therefore improve
their ability to monitor disease progression. Deep learning provides new
avenues of compressing and indexing video in an extremely efficient manner. In
this study, we propose to use an autoencoder for efficient video compression
and fast retrieval of video images. To boost the accuracy of video image
retrieval and to address data variability like multi-modality and view-point
changes, we propose the integration of a Siamese network. We demonstrate that
our approach is competitive in retrieving images from 3 large scale videos of 3
different patients obtained against the query samples of their previous
diagnosis. Quantitative validation shows that the combined approach yield an
overall improvement of 5% and 8% over classical and variational autoencoders,
respectively.Comment: Accepted at IEEE International Symposium on Biomedical Imaging
(ISBI), 201
Foreign Object Detection and Quantification of Fat Content Using A Novel Multiplexing Electric Field Sensor
There is an ever growing need to ensure the quality of food and assess
specific quality parameters in all the links of the food chain, ranging from
processing, distribution and retail to preparing food. Various imaging and
sensing technologies, including X-ray imaging, ultrasound, and near infrared
reflectance spectroscopy have been applied to the problem. Cost and other
constraints restrict the application of some of these technologies. In this
study we test a novel Multiplexing Electric Field Sensor (MEFS), an approach
that allows for a completely non-invasive and non-destructive testing approach.
Our experiments demonstrate the reliable detection of certain foreign objects
and provide evidence that this sensor technology has the capability of
measuring fat content in minced meat. Given the fact that this technology can
already be deployed at very low cost, low maintenance and in various different
form factors, we conclude that this type of MEFS is an extremely promising
technology for addressing specific food quality issues
SSL-CPCD: Self-supervised learning with composite pretext-class discrimination for improved generalisability in endoscopic image analysis
Data-driven methods have shown tremendous progress in medical image analysis.
In this context, deep learning-based supervised methods are widely popular.
However, they require a large amount of training data and face issues in
generalisability to unseen datasets that hinder clinical translation.
Endoscopic imaging data incorporates large inter- and intra-patient variability
that makes these models more challenging to learn representative features for
downstream tasks. Thus, despite the publicly available datasets and datasets
that can be generated within hospitals, most supervised models still
underperform. While self-supervised learning has addressed this problem to some
extent in natural scene data, there is a considerable performance gap in the
medical image domain. In this paper, we propose to explore patch-level
instance-group discrimination and penalisation of inter-class variation using
additive angular margin within the cosine similarity metrics. Our novel
approach enables models to learn to cluster similar representative patches,
thereby improving their ability to provide better separation between different
classes. Our results demonstrate significant improvement on all metrics over
the state-of-the-art (SOTA) methods on the test set from the same and diverse
datasets. We evaluated our approach for classification, detection, and
segmentation. SSL-CPCD achieves 79.77% on Top 1 accuracy for ulcerative colitis
classification, 88.62% on mAP for polyp detection, and 82.32% on dice
similarity coefficient for segmentation tasks are nearly over 4%, 2%, and 3%,
respectively, compared to the baseline architectures. We also demonstrate that
our method generalises better than all SOTA methods to unseen datasets,
reporting nearly 7% improvement in our generalisability assessment.Comment: 1
A deep learning framework for quality assessment and restoration in video endoscopy
Endoscopy is a routine imaging technique used for both diagnosis and
minimally invasive surgical treatment. Artifacts such as motion blur, bubbles,
specular reflections, floating objects and pixel saturation impede the visual
interpretation and the automated analysis of endoscopy videos. Given the
widespread use of endoscopy in different clinical applications, we contend that
the robust and reliable identification of such artifacts and the automated
restoration of corrupted video frames is a fundamental medical imaging problem.
Existing state-of-the-art methods only deal with the detection and restoration
of selected artifacts. However, typically endoscopy videos contain numerous
artifacts which motivates to establish a comprehensive solution.
We propose a fully automatic framework that can: 1) detect and classify six
different primary artifacts, 2) provide a quality score for each frame and 3)
restore mildly corrupted frames. To detect different artifacts our framework
exploits fast multi-scale, single stage convolutional neural network detector.
We introduce a quality metric to assess frame quality and predict image
restoration success. Generative adversarial networks with carefully chosen
regularization are finally used to restore corrupted frames.
Our detector yields the highest mean average precision (mAP at 5% threshold)
of 49.0 and the lowest computational time of 88 ms allowing for accurate
real-time processing. Our restoration models for blind deblurring, saturation
correction and inpainting demonstrate significant improvements over previous
methods. On a set of 10 test videos we show that our approach preserves an
average of 68.7% which is 25% more frames than that retained from the raw
videos.Comment: 14 page
Patch-level instance-group discrimination with pretext-invariant learning for colitis scoring
Inflammatory bowel disease (IBD), in particular ulcerative colitis (UC), is
graded by endoscopists and this assessment is the basis for risk stratification
and therapy monitoring. Presently, endoscopic characterisation is largely
operator dependant leading to sometimes undesirable clinical outcomes for
patients with IBD. We focus on the Mayo Endoscopic Scoring (MES) system which
is widely used but requires the reliable identification of subtle changes in
mucosal inflammation. Most existing deep learning classification methods cannot
detect these fine-grained changes which make UC grading such a challenging
task. In this work, we introduce a novel patch-level instance-group
discrimination with pretext-invariant representation learning (PLD-PIRL) for
self-supervised learning (SSL). Our experiments demonstrate both improved
accuracy and robustness compared to the baseline supervised network and several
state-of-the-art SSL methods. Compared to the baseline (ResNet50) supervised
classification our proposed PLD-PIRL obtained an improvement of 4.75% on
hold-out test data and 6.64% on unseen center test data for top-1 accuracy.Comment: 1
TRAIT2D: a Software for Quantitative Analysis of Single Particle Diffusion Data
Single particle tracking (SPT) is one of the most widely used tools in optical microscopy to evaluate particle mobility in a variety of situations, including cellular and model membrane dynamics. Recent technological developments, such as Interferometric Scattering microscopy, have allowed recording of long, uninterrupted single particle trajectories at kilohertz framerates. The resulting data, where particles are continuously detected and do not displace much between observations, thereby do not require complex linking algorithms. Moreover, while these measurements offer more details into the short-term diffusion behaviour of the tracked particles, they are also subject to the influence of localisation uncertainties, which are often underestimated by conventional analysis pipelines. we thus developed a Python library, under the name of TRAIT2D (Tracking Analysis Toolbox – 2D version), in order to track particle diffusion at high sampling rates, and analyse the resulting trajectories with an innovative approach. The data analysis pipeline introduced is more localisation-uncertainty aware, and also selects the most appropriate diffusion model for the data provided on a statistical basis. A trajectory simulation platform also allows the user to handily generate trajectories and even synthetic time-lapses to test alternative tracking algorithms and data analysis approaches. A high degree of customisation for the analysis pipeline, for example with the introduction of different diffusion modes, is possible from the source code. Finally, the presence of graphical user interfaces lowers the access barrier for users with little to no programming experience
Beyond attention: deriving biologically interpretable insights from weakly-supervised multiple-instance learning models
Recent advances in attention-based multiple instance learning (MIL) have
improved our insights into the tissue regions that models rely on to make
predictions in digital pathology. However, the interpretability of these
approaches is still limited. In particular, they do not report whether
high-attention regions are positively or negatively associated with the class
labels or how well these regions correspond to previously established clinical
and biological knowledge. We address this by introducing a post-training
methodology to analyse MIL models. Firstly, we introduce
prediction-attention-weighted (PAW) maps by combining tile-level attention and
prediction scores produced by a refined encoder, allowing us to quantify the
predictive contribution of high-attention regions. Secondly, we introduce a
biological feature instantiation technique by integrating PAW maps with nuclei
segmentation masks. This further improves interpretability by providing
biologically meaningful features related to the cellular organisation of the
tissue and facilitates comparisons with known clinical features. We illustrate
the utility of our approach by comparing PAW maps obtained for prostate cancer
diagnosis (i.e. samples containing malignant tissue, 381/516 tissue samples)
and prognosis (i.e. samples from patients with biochemical recurrence following
surgery, 98/663 tissue samples) in a cohort of patients from the international
cancer genome consortium (ICGC UK Prostate Group). Our approach reveals that
regions that are predictive of adverse prognosis do not tend to co-locate with
the tumour regions, indicating that non-cancer cells should also be studied
when evaluating prognosis
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