114 research outputs found
FUSC: Fetal Ultrasound Semantic Clustering of Second Trimester Scans Using Deep Self-supervised Learning
Ultrasound is the primary imaging modality in clinical practice during
pregnancy. More than 140M fetuses are born yearly, resulting in numerous scans.
The availability of a large volume of fetal ultrasound scans presents the
opportunity to train robust machine learning models. However, the abundance of
scans also has its challenges, as manual labeling of each image is needed for
supervised methods. Labeling is typically labor-intensive and requires
expertise to annotate the images accurately. This study presents an
unsupervised approach for automatically clustering ultrasound images into a
large range of fetal views, reducing or eliminating the need for manual
labeling. Our Fetal Ultrasound Semantic Clustering (FUSC) method is developed
using a large dataset of 88,063 images and further evaluated on an additional
unseen dataset of 8,187 images achieving over 92% clustering purity. The result
of our investigation hold the potential to significantly impact the field of
fetal ultrasound imaging and pave the way for more advanced automated labeling
solutions. Finally, we make the code and the experimental setup publicly
available to help advance the field
On the Importance of Image Encoding in Automated Chest X-Ray Report Generation
Chest X-ray is one of the most popular medical imaging modalities due to its
accessibility and effectiveness. However, there is a chronic shortage of
well-trained radiologists who can interpret these images and diagnose the
patient's condition. Therefore, automated radiology report generation can be a
very helpful tool in clinical practice. A typical report generation workflow
consists of two main steps: (i) encoding the image into a latent space and (ii)
generating the text of the report based on the latent image embedding. Many
existing report generation techniques use a standard convolutional neural
network (CNN) architecture for image encoding followed by a Transformer-based
decoder for medical text generation. In most cases, CNN and the decoder are
trained jointly in an end-to-end fashion. In this work, we primarily focus on
understanding the relative importance of encoder and decoder components.
Towards this end, we analyze four different image encoding approaches: direct,
fine-grained, CLIP-based, and Cluster-CLIP-based encodings in conjunction with
three different decoders on the large-scale MIMIC-CXR dataset. Among these
encoders, the cluster CLIP visual encoder is a novel approach that aims to
generate more discriminative and explainable representations. CLIP-based
encoders produce comparable results to traditional CNN-based encoders in terms
of NLP metrics, while fine-grained encoding outperforms all other encoders both
in terms of NLP and clinical accuracy metrics, thereby validating the
importance of image encoder to effectively extract semantic information. GitHub
repository: https://github.com/mudabek/encoding-cxr-report-ge
FUSQA: Fetal Ultrasound Segmentation Quality Assessment
Deep learning models have been effective for various fetal ultrasound
segmentation tasks. However, generalization to new unseen data has raised
questions about their effectiveness for clinical adoption. Normally, a
transition to new unseen data requires time-consuming and costly quality
assurance processes to validate the segmentation performance post-transition.
Segmentation quality assessment efforts have focused on natural images, where
the problem has been typically formulated as a dice score regression task. In
this paper, we propose a simplified Fetal Ultrasound Segmentation Quality
Assessment (FUSQA) model to tackle the segmentation quality assessment when no
masks exist to compare with. We formulate the segmentation quality assessment
process as an automated classification task to distinguish between good and
poor-quality segmentation masks for more accurate gestational age estimation.
We validate the performance of our proposed approach on two datasets we collect
from two hospitals using different ultrasound machines. We compare different
architectures, with our best-performing architecture achieving over 90%
classification accuracy on distinguishing between good and poor-quality
segmentation masks from an unseen dataset. Additionally, there was only a
1.45-day difference between the gestational age reported by doctors and
estimated based on CRL measurements using well-segmented masks. On the other
hand, this difference increased and reached up to 7.73 days when we calculated
CRL from the poorly segmented masks. As a result, AI-based approaches can
potentially aid fetal ultrasound segmentation quality assessment and might
detect poor segmentation in real-time screening in the future.Comment: 13 pages, 3 figures, 3 table
Diagnostic challenge of Creutzfeldt-Jakob disease in a patient with multimorbidity:a case-report
BACKGROUND: Creutzfeldt-Jakob disease (CJD) is a rapidly progressive and ultimately fatal neurodegenerative condition caused by prions. The clinical symptoms of CJD vary with its subtype, and may include dementia, visual hallucinations, myoclonus, ataxia, (extra)pyramidal signs and akinetic mutism. In the early course of disease however, several clinical symptoms of CJD may mimic those of co-existing morbidities.CASE PRESENTATION: We report a male in his 60s with a history of situs inversus totalis and Churg Strauss syndrome, who presented with speech fluency disturbances, neuropsychiatric symptoms and allodynia, a few months after becoming a widower. Initially presumed a bereavement disorder along with a flare-up of Churg Strauss, his symptoms gradually worsened with apraxia, myoclonic jerks and eventually, akinetic mutism. MRI revealed hyperintensities at the caudate nucleus and thalami, while the cerebrospinal fluid was positive for the 14-3-3 protein and the real-time quick test, making the diagnosis of CJD highly probable. This case illustrates the complexities that may arise in diagnosing CJD when pre-existing multimorbidity may cloud the clinical presentation. We also discuss the potential mechanisms underlying the co-occurrence of three rare conditions (situs inversus totalis, Churg Strauss syndrome, CJD) in one patient, taking into consideration the possibility of coincidence as well as common underlying factors. CONCLUSIONS: The diagnosis of CJD may be easily missed when its clinical symptoms are obscured by those of pre-existing (rare) multimorbidity. This case highlights that when the multimorbidity has neurological manifestations, an extensive evaluation remains crucial to establish the diagnosis, minimize the risk of prion-transmission and provide appropriate guidance to patients and their caregivers.</p
Improving Performance of Private Federated Models in Medical Image Analysis
Federated learning (FL) is a distributed machine learning (ML) approach that
allows data to be trained without being centralized. This approach is
particularly beneficial for medical applications because it addresses some key
challenges associated with medical data, such as privacy, security, and data
ownership. On top of that, FL can improve the quality of ML models used in
medical applications. Medical data is often diverse and can vary significantly
depending on the patient population, making it challenging to develop ML models
that are accurate and generalizable. FL allows medical data to be used from
multiple sources, which can help to improve the quality and generalizability of
ML models. Differential privacy (DP) is a go-to algorithmic tool to make this
process secure and private. In this work, we show that the model performance
can be further improved by employing local steps, a popular approach to
improving the communication efficiency of FL, and tuning the number of
communication rounds. Concretely, given the privacy budget, we show an optimal
number of local steps and communications rounds. We provide theoretical
motivations further corroborated with experimental evaluations on real-world
medical imaging tasks
Prompt-based Tuning of Transformer Models for Multi-Center Medical Image Segmentation
Medical image segmentation is a vital healthcare endeavor requiring precise
and efficient models for appropriate diagnosis and treatment. Vision
transformer-based segmentation models have shown great performance in
accomplishing this task. However, to build a powerful backbone, the
self-attention block of ViT requires large-scale pre-training data. The present
method of modifying pre-trained models entails updating all or some of the
backbone parameters. This paper proposes a novel fine-tuning strategy for
adapting a pretrained transformer-based segmentation model on data from a new
medical center. This method introduces a small number of learnable parameters,
termed prompts, into the input space (less than 1\% of model parameters) while
keeping the rest of the model parameters frozen. Extensive studies employing
data from new unseen medical centers show that prompts-based fine-tuning of
medical segmentation models provides excellent performance on the new center
data with a negligible drop on the old centers. Additionally, our strategy
delivers great accuracy with minimum re-training on new center data,
significantly decreasing the computational and time costs of fine-tuning
pre-trained models
GARDNet: Robust Multi-View Network for Glaucoma Classification in Color Fundus Images
Glaucoma is one of the most severe eye diseases, characterized by rapid
progression and leading to irreversible blindness. It is often the case that
diagnostics is carried out when one's sight has already significantly degraded
due to the lack of noticeable symptoms at early stage of the disease. Regular
glaucoma screenings of the population shall improve early-stage detection,
however the desirable frequency of etymological checkups is often not feasible
due to the excessive load imposed by manual diagnostics on limited number of
specialists. Considering the basic methodology to detect glaucoma is to analyze
fundus images for the optic-disc-to-optic-cup ratio, Machine Learning
algorithms can offer sophisticated methods for image processing and
classification. In our work, we propose an advanced image pre-processing
technique combined with a multi-view network of deep classification models to
categorize glaucoma. Our Glaucoma Automated Retinal Detection Network (GARDNet)
has been successfully tested on Rotterdam EyePACS AIROGS dataset with an AUC of
0.92, and then additionally fine-tuned and tested on RIM-ONE DL dataset with an
AUC of 0.9308 outperforming the state-of-the-art of 0.9272. Our code is
available on https://github.com/ahmed1996said/gardnetComment: Keywords: Glaucoma Classification, Color Fundus Images. Computer
Aided Diagnosis. Deep Learnin
SEDA: Self-Ensembling ViT with Defensive Distillation and Adversarial Training for robust Chest X-rays Classification
Deep Learning methods have recently seen increased adoption in medical
imaging applications. However, elevated vulnerabilities have been explored in
recent Deep Learning solutions, which can hinder future adoption. Particularly,
the vulnerability of Vision Transformer (ViT) to adversarial, privacy, and
confidentiality attacks raise serious concerns about their reliability in
medical settings. This work aims to enhance the robustness of self-ensembling
ViTs for the tuberculosis chest x-ray classification task. We propose
Self-Ensembling ViT with defensive Distillation and Adversarial training
(SEDA). SEDA utilizes efficient CNN blocks to learn spatial features with
various levels of abstraction from feature representations extracted from
intermediate ViT blocks, that are largely unaffected by adversarial
perturbations. Furthermore, SEDA leverages adversarial training in combination
with defensive distillation for improved robustness against adversaries.
Training using adversarial examples leads to better model generalizability and
improves its ability to handle perturbations. Distillation using soft
probabilities introduces uncertainty and variation into the output
probabilities, making it more difficult for adversarial and privacy attacks.
Extensive experiments performed with the proposed architecture and training
paradigm on publicly available Tuberculosis x-ray dataset shows SOTA efficacy
of SEDA compared to SEViT in terms of computational efficiency with 70x times
lighter framework and enhanced robustness of +9%.Comment: Accepted at DART (Domain Adaptation and Representation Transfer)
Workshop, MICCAI, 2023. Code: https://github.com/Razaimam45/SED
Automatic C-Plane Detection in Pelvic Floor Transperineal Volumetric Ultrasound
© 2020, Springer Nature Switzerland AG. Transperineal volumetric ultrasound (US) imaging has become routine practice for diagnosing pelvic floor disease (PFD). Hereto, clinical guidelines stipulate to make measurements in an anatomically defined 2D plane within a 3D volume, the so-called C-plane. This task is currently performed manually in clinical practice, which is labour-intensive and requires expert knowledge of pelvic floor anatomy, as no computer-aided C-plane method exists. To automate this process, we propose a novel, guideline-driven approach for automatic detection of the C-plane. The method uses a convolutional neural network (CNN) to identify extreme coordinates of the symphysis pubis and levator ani muscle (which define the C-plane) directly via landmark regression. The C-plane is identified in a postprocessing step. When evaluated on 100 US volumes, our best performing method (multi-task regression with UNet) achieved a mean error of 6.05 mm and 4.81 and took 20 s. Two experts blindly evaluated the quality of the automatically detected planes and manually defined the (gold standard) C-plane in terms of their clinical diagnostic quality. We show that the proposed method performs comparably to the manual definition. The automatic method reduces the average time to detect the C-plane by 100 s and reduces the need for high-level expertise in PFD US assessment
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