98 research outputs found
Pre and Post-hoc Diagnosis and Interpretation of Malignancy from Breast DCE-MRI
We propose a new method for breast cancer screening from DCE-MRI based on a
post-hoc approach that is trained using weakly annotated data (i.e., labels are
available only at the image level without any lesion delineation). Our proposed
post-hoc method automatically diagnosis the whole volume and, for positive
cases, it localizes the malignant lesions that led to such diagnosis.
Conversely, traditional approaches follow a pre-hoc approach that initially
localises suspicious areas that are subsequently classified to establish the
breast malignancy -- this approach is trained using strongly annotated data
(i.e., it needs a delineation and classification of all lesions in an image).
Another goal of this paper is to establish the advantages and disadvantages of
both approaches when applied to breast screening from DCE-MRI. Relying on
experiments on a breast DCE-MRI dataset that contains scans of 117 patients,
our results show that the post-hoc method is more accurate for diagnosing the
whole volume per patient, achieving an AUC of 0.91, while the pre-hoc method
achieves an AUC of 0.81. However, the performance for localising the malignant
lesions remains challenging for the post-hoc method due to the weakly labelled
dataset employed during training.Comment: Submitted to Medical Image Analysi
Model Agnostic Saliency for Weakly Supervised Lesion Detection from Breast DCE-MRI
There is a heated debate on how to interpret the decisions provided by deep
learning models (DLM), where the main approaches rely on the visualization of
salient regions to interpret the DLM classification process. However, these
approaches generally fail to satisfy three conditions for the problem of lesion
detection from medical images: 1) for images with lesions, all salient regions
should represent lesions, 2) for images containing no lesions, no salient
region should be produced,and 3) lesions are generally small with relatively
smooth borders. We propose a new model-agnostic paradigm to interpret DLM
classification decisions supported by a novel definition of saliency that
incorporates the conditions above. Our model-agnostic 1-class saliency detector
(MASD) is tested on weakly supervised breast lesion detection from DCE-MRI,
achieving state-of-the-art detection accuracy when compared to current
visualization methods
Training Medical Image Analysis Systems like Radiologists
The training of medical image analysis systems using machine learning
approaches follows a common script: collect and annotate a large dataset, train
the classifier on the training set, and test it on a hold-out test set. This
process bears no direct resemblance with radiologist training, which is based
on solving a series of tasks of increasing difficulty, where each task involves
the use of significantly smaller datasets than those used in machine learning.
In this paper, we propose a novel training approach inspired by how
radiologists are trained. In particular, we explore the use of meta-training
that models a classifier based on a series of tasks. Tasks are selected using
teacher-student curriculum learning, where each task consists of simple
classification problems containing small training sets. We hypothesize that our
proposed meta-training approach can be used to pre-train medical image analysis
models. This hypothesis is tested on the automatic breast screening
classification from DCE-MRI trained with weakly labeled datasets. The
classification performance achieved by our approach is shown to be the best in
the field for that application, compared to state of art baseline approaches:
DenseNet, multiple instance learning and multi-task learning.Comment: Oral Presentation at MICCAI 201
Multi Scale Curriculum CNN for Context-Aware Breast MRI Malignancy Classification
Classification of malignancy for breast cancer and other cancer types is
usually tackled as an object detection problem: Individual lesions are first
localized and then classified with respect to malignancy. However, the drawback
of this approach is that abstract features incorporating several lesions and
areas that are not labelled as a lesion but contain global medically relevant
information are thus disregarded: especially for dynamic contrast-enhanced
breast MRI, criteria such as background parenchymal enhancement and location
within the breast are important for diagnosis and cannot be captured by object
detection approaches properly.
In this work, we propose a 3D CNN and a multi scale curriculum learning
strategy to classify malignancy globally based on an MRI of the whole breast.
Thus, the global context of the whole breast rather than individual lesions is
taken into account. Our proposed approach does not rely on lesion
segmentations, which renders the annotation of training data much more
effective than in current object detection approaches.
Achieving an AUROC of 0.89, we compare the performance of our approach to
Mask R-CNN and Retina U-Net as well as a radiologist. Our performance is on par
with approaches that, in contrast to our method, rely on pixelwise
segmentations of lesions.Comment: Accepted to MICCAI 201
Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions
Breast cancer has reached the highest incidence rate worldwide among all
malignancies since 2020. Breast imaging plays a significant role in early
diagnosis and intervention to improve the outcome of breast cancer patients. In
the past decade, deep learning has shown remarkable progress in breast cancer
imaging analysis, holding great promise in interpreting the rich information
and complex context of breast imaging modalities. Considering the rapid
improvement in the deep learning technology and the increasing severity of
breast cancer, it is critical to summarize past progress and identify future
challenges to be addressed. In this paper, we provide an extensive survey of
deep learning-based breast cancer imaging research, covering studies on
mammogram, ultrasound, magnetic resonance imaging, and digital pathology images
over the past decade. The major deep learning methods, publicly available
datasets, and applications on imaging-based screening, diagnosis, treatment
response prediction, and prognosis are described in detail. Drawn from the
findings of this survey, we present a comprehensive discussion of the
challenges and potential avenues for future research in deep learning-based
breast cancer imaging.Comment: Survey, 41 page
Deep learning in medical imaging and radiation therapy
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/1/mp13264_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/2/mp13264.pd
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Annotation-efficient cancer detection with report-guided lesion annotation for deep learning-based prostate cancer detection in bpMRI
Deep learning-based diagnostic performance increases with more annotated
data, but large-scale manual annotations are expensive and labour-intensive.
Experts evaluate diagnostic images during clinical routine, and write their
findings in reports. Leveraging unlabelled exams paired with clinical reports
could overcome the manual labelling bottleneck. We hypothesise that detection
models can be trained semi-supervised with automatic annotations generated
using model predictions, guided by sparse information from clinical reports. To
demonstrate efficacy, we train clinically significant prostate cancer (csPCa)
segmentation models, where automatic annotations are guided by the number of
clinically significant findings in the radiology reports. We included 7,756
prostate MRI examinations, of which 3,050 were manually annotated. We evaluated
prostate cancer detection performance on 300 exams from an external centre with
histopathology-confirmed ground truth. Semi-supervised training improved
patient-based diagnostic area under the receiver operating characteristic curve
from to () and improved
lesion-based sensitivity at one false positive per case from
to (). Semi-supervised training was 14 more
annotation-efficient for case-based performance and 6 more
annotation-efficient for lesion-based performance. This improved performance
demonstrates the feasibility of our training procedure. Source code is publicly
available at github.com/DIAGNijmegen/Report-Guided-Annotation. Best csPCa
detection algorithm is available at
grand-challenge.org/algorithms/bpmri-cspca-detection-report-guided-annotations/
- …