85 research outputs found
Improving Object Detection in Medical Image Analysis through Multiple Expert Annotators: An Empirical Investigation
The work discusses the use of machine learning algorithms for anomaly
detection in medical image analysis and how the performance of these algorithms
depends on the number of annotators and the quality of labels. To address the
issue of subjectivity in labeling with a single annotator, we introduce a
simple and effective approach that aggregates annotations from multiple
annotators with varying levels of expertise. We then aim to improve the
efficiency of predictive models in abnormal detection tasks by estimating
hidden labels from multiple annotations and using a re-weighted loss function
to improve detection performance. Our method is evaluated on a real-world
medical imaging dataset and outperforms relevant baselines that do not consider
disagreements among annotators.Comment: This is a short version submitted to the Midwest Machine Learning
Symposium (MMLS 2023), Chicago, IL, US
Multimodal contrastive learning for diagnosing cardiovascular diseases from electrocardiography (ECG) signals and patient metadata
This work discusses the use of contrastive learning and deep learning for
diagnosing cardiovascular diseases from electrocardiography (ECG) signals.
While the ECG signals usually contain 12 leads (channels), many healthcare
facilities and devices lack access to all these 12 leads. This raises the
problem of how to use only fewer ECG leads to produce meaningful diagnoses with
high performance. We introduce a simple experiment to test whether contrastive
learning can be applied to this task. More specifically, we added the
similarity between the embedding vectors when the 12 leads signal and the fewer
leads ECG signal to the loss function to bring these representations closer
together. Despite its simplicity, this has been shown to have improved the
performance of diagnosing with all lead combinations, proving the potential of
contrastive learning on this task.Comment: Accepted for presentation at the Midwest Machine Learning Symposium
(MMLS 2023), Chicago, IL, US
Enhancing Few-shot Image Classification with Cosine Transformer
This paper addresses the few-shot image classification problem, where the
classification task is performed on unlabeled query samples given a small
amount of labeled support samples only. One major challenge of the few-shot
learning problem is the large variety of object visual appearances that
prevents the support samples to represent that object comprehensively. This
might result in a significant difference between support and query samples,
therefore undermining the performance of few-shot algorithms. In this paper, we
tackle the problem by proposing Few-shot Cosine Transformer (FS-CT), where the
relational map between supports and queries is effectively obtained for the
few-shot tasks. The FS-CT consists of two parts, a learnable prototypical
embedding network to obtain categorical representations from support samples
with hard cases, and a transformer encoder to effectively achieve the
relational map from two different support and query samples. We introduce
Cosine Attention, a more robust and stable attention module that enhances the
transformer module significantly and therefore improves FS-CT performance from
5% to over 20% in accuracy compared to the default scaled dot-product
mechanism. Our method performs competitive results in mini-ImageNet, CUB-200,
and CIFAR-FS on 1-shot learning and 5-shot learning tasks across backbones and
few-shot configurations. We also developed a custom few-shot dataset for Yoga
pose recognition to demonstrate the potential of our algorithm for practical
application. Our FS-CT with cosine attention is a lightweight, simple few-shot
algorithm that can be applied for a wide range of applications, such as
healthcare, medical, and security surveillance. The official implementation
code of our Few-shot Cosine Transformer is available at
https://github.com/vinuni-vishc/Few-Shot-Cosine-Transforme
Slice-level Detection of Intracranial Hemorrhage on CT Using Deep Descriptors of Adjacent Slices
The rapid development in representation learning techniques such as deep
neural networks and the availability of large-scale, well-annotated medical
imaging datasets have to a rapid increase in the use of supervised machine
learning in the 3D medical image analysis and diagnosis. In particular, deep
convolutional neural networks (D-CNNs) have been key players and were adopted
by the medical imaging community to assist clinicians and medical experts in
disease diagnosis and treatment. However, training and inferencing deep neural
networks such as D-CNN on high-resolution 3D volumes of Computed Tomography
(CT) scans for diagnostic tasks pose formidable computational challenges. This
challenge raises the need of developing deep learning-based approaches that are
robust in learning representations in 2D images, instead 3D scans. In this
work, we propose for the first time a new strategy to train \emph{slice-level}
classifiers on CT scans based on the descriptors of the adjacent slices along
the axis. In particular, each of which is extracted through a convolutional
neural network (CNN). This method is applicable to CT datasets with per-slice
labels such as the RSNA Intracranial Hemorrhage (ICH) dataset, which aims to
predict the presence of ICH and classify it into 5 different sub-types. We
obtain a single model in the top 4% best-performing solutions of the RSNA ICH
challenge, where model ensembles are allowed. Experiments also show that the
proposed method significantly outperforms the baseline model on CQ500. The
proposed method is general and can be applied to other 3D medical diagnosis
tasks such as MRI imaging. To encourage new advances in the field, we will make
our codes and pre-trained model available upon acceptance of the paper.Comment: Accepted for presentation at the 22nd IEEE Statistical Signal
Processing (SSP) worksho
FedDCT: Federated Learning of Large Convolutional Neural Networks on Resource Constrained Devices using Divide and Co-Training
We introduce FedDCT, a novel distributed learning paradigm that enables the
usage of large, high-performance CNNs on resource-limited edge devices. As
opposed to traditional FL approaches, which require each client to train the
full-size neural network independently during each training round, the proposed
FedDCT allows a cluster of several clients to collaboratively train a large
deep learning model by dividing it into an ensemble of several small sub-models
and train them on multiple devices in parallel while maintaining privacy. In
this co-training process, clients from the same cluster can also learn from
each other, further improving their ensemble performance. In the aggregation
stage, the server takes a weighted average of all the ensemble models trained
by all the clusters. FedDCT reduces the memory requirements and allows low-end
devices to participate in FL. We empirically conduct extensive experiments on
standardized datasets, including CIFAR-10, CIFAR-100, and two real-world
medical datasets HAM10000 and VAIPE. Experimental results show that FedDCT
outperforms a set of current SOTA FL methods with interesting convergence
behaviors. Furthermore, compared to other existing approaches, FedDCT achieves
higher accuracy and substantially reduces the number of communication rounds
(with times fewer memory requirements) to achieve the desired accuracy on
the testing dataset without incurring any extra training cost on the server
side.Comment: Under review by the IEEE Transactions on Network and Service
Managemen
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