18 research outputs found
TransRUPNet for Improved Out-of-Distribution Generalization in Polyp Segmentation
Out-of-distribution (OOD) generalization is a critical challenge in deep
learning. It is specifically important when the test samples are drawn from a
different distribution than the training data. We develop a novel real-time
deep learning based architecture, TransRUPNet that is based on a Transformer
and residual upsampling network for colorectal polyp segmentation to improve
OOD generalization. The proposed architecture, TransRUPNet, is an
encoder-decoder network that consists of three encoder blocks, three decoder
blocks, and some additional upsampling blocks at the end of the network. With
the image size of , the proposed method achieves an excellent
real-time operation speed of \textbf{47.07} frames per second with an average
mean dice coefficient score of 0.7786 and mean Intersection over Union of
0.7210 on the out-of-distribution polyp datasets. The results on the publicly
available PolypGen dataset (OOD dataset in our case) suggest that TransRUPNet
can give real-time feedback while retaining high accuracy for in-distribution
dataset. Furthermore, we demonstrate the generalizability of the proposed
method by showing that it significantly improves performance on OOD datasets
compared to the existing methods
RUPNet: Residual upsampling network for real-time polyp segmentation
Colorectal cancer is among the most prevalent cause of cancer-related
mortality worldwide. Detection and removal of polyps at an early stage can help
reduce mortality and even help in spreading over adjacent organs. Early polyp
detection could save the lives of millions of patients over the world as well
as reduce the clinical burden. However, the detection polyp rate varies
significantly among endoscopists. There is numerous deep learning-based method
proposed, however, most of the studies improve accuracy. Here, we propose a
novel architecture, Residual Upsampling Network (RUPNet) for colon polyp
segmentation that can process in real-time and show high recall and precision.
The proposed architecture, RUPNet, is an encoder-decoder network that consists
of three encoders, three decoder blocks, and some additional upsampling blocks
at the end of the network. With an image size of , the proposed
method achieves an excellent real-time operation speed of 152.60 frames per
second with an average dice coefficient of 0.7658, mean intersection of union
of 0.6553, sensitivity of 0.8049, precision of 0.7995, and F2-score of 0.9361.
The results suggest that RUPNet can give real-time feedback while retaining
high accuracy indicating a good benchmark for early polyp detection.Comment: Accepted SPIE Medical Imaging 202
Prevalence of vitamin D deficiency and its relationship with thyroid autoimmunity in Asian Indians: a community-based survey
25-Hydroxy vitamin D (25(OH)D) deficiency is linked with predisposition to autoimmune type 1 diabetes and multiple sclerosis. Our objective was to assess the relationship between serum 25(OH)D levels and thyroid autoimmunity. Subjects included students, teachers and staff aged 16-60 years (total 642, 244 males, 398 females). Serum free thyroxine, thyroid-stimulating hormone (TSH), and thyroid peroxidase autoantibodies (TPOAb), intact parathyroid hormone and 25(OH)D were measured by electrochemiluminescence and RIA, respectively. Thyroid dysfunction was defined if (1) serum TSH ≥ 5 μ U/ml and TPOAb>34 IU/ml or (2) TSH ≥ 10 μ U/ml but normal TPOAb. The mean serum 25(OH)D of the study subjects was 17.5 (SD 10.2) nmol/l with 87 % having values ≤ 25 nmol/l. TPOAb positivity was observed in 21 % of subjects. The relationship between 25(OH)D and TPOAb was assessed with and without controlling for age and showed significant inverse correlation (r - 0.08, P = 0.04) when adjusted for age. The prevalence of TPOAb and thyroid dysfunction were comparable between subjects stratified according to serum 25(OH)D into two groups either at cut-off of ≤ 25 or >25 nmol/l or first and second tertiles. Serum 25(OH)D values show only weak inverse correlation with TPOAb titres. The presence of such weak association and narrow range of serum 25(OH)D did not allow us to interpret the present results in terms of quantitative cut-off values of serum 25(OH)D. Further studies in vitamin D-sufficient populations with wider range of serum 25(OH)D levels are required to substantiate the findings of the current study
GastroVision: A Multi-class Endoscopy Image Dataset for Computer Aided Gastrointestinal Disease Detection
Integrating real-time artificial intelligence (AI) systems in clinical
practices faces challenges such as scalability and acceptance. These challenges
include data availability, biased outcomes, data quality, lack of transparency,
and underperformance on unseen datasets from different distributions. The
scarcity of large-scale, precisely labeled, and diverse datasets are the major
challenge for clinical integration. This scarcity is also due to the legal
restrictions and extensive manual efforts required for accurate annotations
from clinicians. To address these challenges, we present \textit{GastroVision},
a multi-center open-access gastrointestinal (GI) endoscopy dataset that
includes different anatomical landmarks, pathological abnormalities, polyp
removal cases and normal findings (a total of 27 classes) from the GI tract.
The dataset comprises 8,000 images acquired from B{\ae}rum Hospital in Norway
and Karolinska University Hospital in Sweden and was annotated and verified by
experienced GI endoscopists. Furthermore, we validate the significance of our
dataset with extensive benchmarking based on the popular deep learning based
baseline models. We believe our dataset can facilitate the development of
AI-based algorithms for GI disease detection and classification. Our dataset is
available at \url{https://osf.io/84e7f/}
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ConvSegNet: Automated Polyp Segmentation From Colonoscopy Using Context Feature Refinement With Multiple Convolutional Kernel Sizes
Colorectal cancer occurs in the rectal of humans, and early detection has been proved to reduce its mortality rate. Colonoscopy is the standard used in detecting the presence of polyps in the rectal, and accurate segmentation of the polyps from colonoscopy images often provides helpful information for early diagnosis and treatment. Although existing deep learning models often achieve high segmentation performance when tested on the same dataset used in model training; still, their performance often degrades when applied to out-of-distribution datasets, leading to low model generalization or overfitting. This challenge is often associated with the quality of the features learnt from the input images. In this work, a novel Context Feature Refinement (CFR) module is proposed to address the challenge of low model generalization and segmentation performance. The CFR module is built to extract contextual information from the incoming feature map by using multiple parallel convolutional layers with progressively increasing kernel sizes. Using multiple parallel convolutions with different kernel sizes helped to extract more efficient multi-scale contextual information and thus enabled the network to effectively identify and segment small and fine details, as well as larger and more complex structures in the input images. Extensive experiments on three public benchmark datasets in CVC-ClinicDB, Kvasir-SEG, and BKAI-NeoPolyp showed that the proposed ConvSegNet model achieved jaccard, dice and F2 scores of 0.8650, 0.9177, and 0.9328 on CVC-ClinicDB, 0.7936, 0.8618, and 0.8855 on Kvasir-SEG, and 0.8045, 0.8747 and 0.8909 on BKAI-NeoPolyp datasets respectively. Also, an improved generalization performance was achieved by the ConvSegNet model, compared to the benchmark polyp segmentation models. Code is available at https://github.com/AOige/ConvSegNet
Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge
Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology. There exists a high missed detection rate and incomplete removal of colonic polyps. To assist in clinical procedures and reduce missed rates, automated methods for detecting and segmenting polyps using machine learning have been achieved in past years. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets from different centres, populations, modalities, and acquisition systems. To test this hypothesis rigorously, we, together with expert gastroenterologists, curated a multi-centre and multi-population dataset acquired from six different colonoscopy systems and challenged the computational expert teams to develop robust automated detection and segmentation methods in a crowd-sourcing Endoscopic computer vision challenge. This work put forward rigorous generalisability tests and assesses the usability of devised deep learning methods in dynamic and actual clinical colonoscopy procedures. We analyse the results of four top performing teams for the detection task and five top performing teams for the segmentation task. Our analyses demonstrate that the top-ranking teams concentrated mainly on accuracy over the real-time performance required for clinical applicability. We further dissect the devised methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets and routine clinical procedures
An objective validation of polyp and instrument segmentation methods in colonoscopy through Medico 2020 polyp segmentation and MedAI 2021 transparency challenges
Automatic analysis of colonoscopy images has been an active field of research
motivated by the importance of early detection of precancerous polyps. However,
detecting polyps during the live examination can be challenging due to various
factors such as variation of skills and experience among the endoscopists, lack
of attentiveness, and fatigue leading to a high polyp miss-rate. Deep learning
has emerged as a promising solution to this challenge as it can assist
endoscopists in detecting and classifying overlooked polyps and abnormalities
in real time. In addition to the algorithm's accuracy, transparency and
interpretability are crucial to explaining the whys and hows of the algorithm's
prediction. Further, most algorithms are developed in private data, closed
source, or proprietary software, and methods lack reproducibility. Therefore,
to promote the development of efficient and transparent methods, we have
organized the "Medico automatic polyp segmentation (Medico 2020)" and "MedAI:
Transparency in Medical Image Segmentation (MedAI 2021)" competitions. We
present a comprehensive summary and analyze each contribution, highlight the
strength of the best-performing methods, and discuss the possibility of
clinical translations of such methods into the clinic. For the transparency
task, a multi-disciplinary team, including expert gastroenterologists, accessed
each submission and evaluated the team based on open-source practices, failure
case analysis, ablation studies, usability and understandability of evaluations
to gain a deeper understanding of the models' credibility for clinical
deployment. Through the comprehensive analysis of the challenge, we not only
highlight the advancements in polyp and surgical instrument segmentation but
also encourage qualitative evaluation for building more transparent and
understandable AI-based colonoscopy systems
A rare presentation of methanol toxicity
Methanol is a highly toxic alcohol resembling ethanol in smell and taste. Methanol poisoning is a lethal form of poisoning that can cause severe metabolic acidosis, visual disturbances, and neurological deficit. Brain lesions typically described in methanol toxicity are in the form of hemorrhagic and non-hemorrhagic necrosis of the basal ganglia and sub-cortical white matter. To our knowledge, lesions in the parietal, temporal, or frontal areas of cerebrum and cerebellar hemispheres have been rarely reported so far. We herewith report this rare presentation