24 research outputs found
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
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
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
Few-Shot Anomaly Detection for Polyp Frames from Colonoscopy
Anomaly detection methods generally target the learning of a normal image
distribution (i.e., inliers showing healthy cases) and during testing, samples
relatively far from the learned distribution are classified as anomalies (i.e.,
outliers showing disease cases). These approaches tend to be sensitive to
outliers that lie relatively close to inliers (e.g., a colonoscopy image with a
small polyp). In this paper, we address the inappropriate sensitivity to
outliers by also learning from inliers. We propose a new few-shot anomaly
detection method based on an encoder trained to maximise the mutual information
between feature embeddings and normal images, followed by a few-shot score
inference network, trained with a large set of inliers and a substantially
smaller set of outliers. We evaluate our proposed method on the clinical
problem of detecting frames containing polyps from colonoscopy video sequences,
where the training set has 13350 normal images (i.e., without polyps) and less
than 100 abnormal images (i.e., with polyps). The results of our proposed model
on this data set reveal a state-of-the-art detection result, while the
performance based on different number of anomaly samples is relatively stable
after approximately 40 abnormal training images.Comment: Accept at MICCAI 202
BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis
This paper describes the field research, design and comparative deployment of
a multimodal medical imaging user interface for breast screening. The main
contributions described here are threefold: 1) The design of an advanced visual
interface for multimodal diagnosis of breast cancer (BreastScreening); 2)
Insights from the field comparison of single vs multimodality screening of
breast cancer diagnosis with 31 clinicians and 566 images, and 3) The
visualization of the two main types of breast lesions in the following image
modalities: (i) MammoGraphy (MG) in both Craniocaudal (CC) and Mediolateral
oblique (MLO) views; (ii) UltraSound (US); and (iii) Magnetic Resonance Imaging
(MRI). We summarize our work with recommendations from the radiologists for
guiding the future design of medical imaging interfaces.Comment: AVI 2020 Short Papers, 5 pages, 2 figures, for associated files, see
https://github.com/MIMBCD-UI/avi-2020-short-pape