225 research outputs found
Thoracic Disease Identification and Localization with Limited Supervision
Accurate identification and localization of abnormalities from radiology
images play an integral part in clinical diagnosis and treatment planning.
Building a highly accurate prediction model for these tasks usually requires a
large number of images manually annotated with labels and finding sites of
abnormalities. In reality, however, such annotated data are expensive to
acquire, especially the ones with location annotations. We need methods that
can work well with only a small amount of location annotations. To address this
challenge, we present a unified approach that simultaneously performs disease
identification and localization through the same underlying model for all
images. We demonstrate that our approach can effectively leverage both class
information as well as limited location annotation, and significantly
outperforms the comparative reference baseline in both classification and
localization tasks.Comment: Conference on Computer Vision and Pattern Recognition 2018 (CVPR
2018). V1: CVPR submission; V2: +supplementary; V3: CVPR camera-ready; V4:
correction, update reference baseline results according to their latest post;
V5: minor correction; V6: Identification results using NIH data splits and
various image model
Did You Get What You Paid For? Rethinking Annotation Cost of Deep Learning Based Computer Aided Detection in Chest Radiographs
As deep networks require large amounts of accurately labeled training data, a
strategy to collect sufficiently large and accurate annotations is as important
as innovations in recognition methods. This is especially true for building
Computer Aided Detection (CAD) systems for chest X-rays where domain expertise
of radiologists is required to annotate the presence and location of
abnormalities on X-ray images. However, there lacks concrete evidence that
provides guidance on how much resource to allocate for data annotation such
that the resulting CAD system reaches desired performance. Without this
knowledge, practitioners often fall back to the strategy of collecting as much
detail as possible on as much data as possible which is cost inefficient. In
this work, we investigate how the cost of data annotation ultimately impacts
the CAD model performance on classification and segmentation of chest
abnormalities in frontal-view X-ray images. We define the cost of annotation
with respect to the following three dimensions: quantity, quality and
granularity of labels. Throughout this study, we isolate the impact of each
dimension on the resulting CAD model performance on detecting 10 chest
abnormalities in X-rays. On a large scale training data with over 120K X-ray
images with gold-standard annotations, we find that cost-efficient annotations
provide great value when collected in large amounts and lead to competitive
performance when compared to models trained with only gold-standard
annotations. We also find that combining large amounts of cost efficient
annotations with only small amounts of expensive labels leads to competitive
CAD models at a much lower cost.Comment: MICCAI 2022, Contains Supplemental Materia
Self-Guided Multiple Instance Learning for Weakly Supervised Disease Classification and Localization in Chest Radiographs
The lack of fine-grained annotations hinders the deployment of automated
diagnosis systems, which require human-interpretable justification for their
decision process. In this paper, we address the problem of weakly supervised
identification and localization of abnormalities in chest radiographs. To that
end, we introduce a novel loss function for training convolutional neural
networks increasing the \emph{localization confidence} and assisting the
overall \emph{disease identification}. The loss leverages both image- and
patch-level predictions to generate auxiliary supervision. Rather than forming
strictly binary from the predictions as done in previous loss formulations, we
create targets in a more customized manner, which allows the loss to account
for possible misclassification. We show that the supervision provided within
the proposed learning scheme leads to better performance and more precise
predictions on prevalent datasets for multiple-instance learning as well as on
the NIH~ChestX-Ray14 benchmark for disease recognition than previously used
losses
Detailed Annotations of Chest X-Rays via CT Projection for Report Understanding
In clinical radiology reports, doctors capture important information about
the patient's health status. They convey their observations from raw medical
imaging data about the inner structures of a patient. As such, formulating
reports requires medical experts to possess wide-ranging knowledge about
anatomical regions with their normal, healthy appearance as well as the ability
to recognize abnormalities. This explicit grasp on both the patient's anatomy
and their appearance is missing in current medical image-processing systems as
annotations are especially difficult to gather. This renders the models to be
narrow experts e.g. for identifying specific diseases. In this work, we recover
this missing link by adding human anatomy into the mix and enable the
association of content in medical reports to their occurrence in associated
imagery (medical phrase grounding). To exploit anatomical structures in this
scenario, we present a sophisticated automatic pipeline to gather and integrate
human bodily structures from computed tomography datasets, which we incorporate
in our PAXRay: A Projected dataset for the segmentation of Anatomical
structures in X-Ray data. Our evaluation shows that methods that take advantage
of anatomical information benefit heavily in visually grounding radiologists'
findings, as our anatomical segmentations allow for up to absolute 50% better
grounding results on the OpenI dataset as compared to commonly used region
proposals. The PAXRay dataset is available at
https://constantinseibold.github.io/paxray/.Comment: 33rd British Machine Vision Conference (BMVC 2022
Self-Guided Multiple Instance Learning for Weakly Supervised Thoracic DiseaseClassification and Localizationin Chest Radiographs
Due to the high complexity of medical images and the scarcity of trained personnel, most large-scale radiological datasets are lacking fine-grained annotations and are often only described on image-level. These shortcomings hinder the deployment of automated diagnosis systems, which require human-interpretable justification for their decision process. In this paper, we address the problem of weakly supervised identification and localization of abnormalities in chest radiographs in a multiple-instance learning setting. To that end, we introduce a novel loss function for training convolutional neural networks increasing the localization confidence and assisting the overall disease identification. The loss leverages both image-and patch-level predictions to generate auxiliary supervision and enables specific training at patch-level. Rather than forming strictly binary from the predictions as done in previous loss formulations, we create targets in a more customized manner. This way, the loss accounts for possible misclassification of less certain instances. We show that the supervision provided within the proposed learning scheme leads to better performance and more precise predictions on prevalent datasets for multiple-instance learning as well as on the NIH ChestX-Ray14 benchmark for disease recognition than previously used losses
Rethinking annotation granularity for overcoming deep shortcut learning: A retrospective study on chest radiographs
Deep learning has demonstrated radiograph screening performances that are
comparable or superior to radiologists. However, recent studies show that deep
models for thoracic disease classification usually show degraded performance
when applied to external data. Such phenomena can be categorized into shortcut
learning, where the deep models learn unintended decision rules that can fit
the identically distributed training and test set but fail to generalize to
other distributions. A natural way to alleviate this defect is explicitly
indicating the lesions and focusing the model on learning the intended
features. In this paper, we conduct extensive retrospective experiments to
compare a popular thoracic disease classification model, CheXNet, and a
thoracic lesion detection model, CheXDet. We first showed that the two models
achieved similar image-level classification performance on the internal test
set with no significant differences under many scenarios. Meanwhile, we found
incorporating external training data even led to performance degradation for
CheXNet. Then, we compared the models' internal performance on the lesion
localization task and showed that CheXDet achieved significantly better
performance than CheXNet even when given 80% less training data. By further
visualizing the models' decision-making regions, we revealed that CheXNet
learned patterns other than the target lesions, demonstrating its shortcut
learning defect. Moreover, CheXDet achieved significantly better external
performance than CheXNet on both the image-level classification task and the
lesion localization task. Our findings suggest improving annotation granularity
for training deep learning systems as a promising way to elevate future deep
learning-based diagnosis systems for clinical usage.Comment: 22 pages of main text, 18 pages of supplementary table
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