11 research outputs found
SimPLe: Similarity-Aware Propagation Learning for Weakly-Supervised Breast Cancer Segmentation in DCE-MRI
Breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays
an important role in the screening and prognosis assessment of high-risk breast
cancer. The segmentation of cancerous regions is essential useful for the
subsequent analysis of breast MRI. To alleviate the annotation effort to train
the segmentation networks, we propose a weakly-supervised strategy using
extreme points as annotations for breast cancer segmentation. Without using any
bells and whistles, our strategy focuses on fully exploiting the learning
capability of the routine training procedure, i.e., the train - fine-tune -
retrain process. The network first utilizes the pseudo-masks generated using
the extreme points to train itself, by minimizing a contrastive loss, which
encourages the network to learn more representative features for cancerous
voxels. Then the trained network fine-tunes itself by using a similarity-aware
propagation learning (SimPLe) strategy, which leverages feature similarity
between unlabeled and positive voxels to propagate labels. Finally the network
retrains itself by employing the pseudo-masks generated using previous
fine-tuned network. The proposed method is evaluated on our collected DCE-MRI
dataset containing 206 patients with biopsy-proven breast cancers. Experimental
results demonstrate our method effectively fine-tunes the network by using the
SimPLe strategy, and achieves a mean Dice value of 81%
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
Deep Group-wise Variational Diffeomorphic Image Registration
Deep neural networks are increasingly used for pair-wise image registration.
We propose to extend current learning-based image registration to allow
simultaneous registration of multiple images. To achieve this, we build upon
the pair-wise variational and diffeomorphic VoxelMorph approach and present a
general mathematical framework that enables both registration of multiple
images to their geodesic average and registration in which any of the available
images can be used as a fixed image. In addition, we provide a likelihood based
on normalized mutual information, a well-known image similarity metric in
registration, between multiple images, and a prior that allows for explicit
control over the viscous fluid energy to effectively regularize deformations.
We trained and evaluated our approach using intra-patient registration of
breast MRI and Thoracic 4DCT exams acquired over multiple time points.
Comparison with Elastix and VoxelMorph demonstrates competitive quantitative
performance of the proposed method in terms of image similarity and reference
landmark distances at significantly faster registration
Multi-Modality Breast MRI Segmentation Using nn-UNet for Preoperative Planning of Robotic Surgery Navigation
Segmentation of the chest region and breast tissues is essential for surgery planning and navigation. This paper proposes the foundation for preoperative segmentation based on two cascaded architectures of deep neural networks (DNN) based on the state-of-the-art nnU-Net. Additionally, this study introduces a polyvinyl alcohol cryogel (PVA-C) breast phantom based on the segmentation of the DNN automated approach, enabling the experiments of navigation system for robotic breast surgery. Multi-modality breast MRI datasets of T2W and STIR images were acquired from 10 patients. Segmentation evaluation utilized the Dice Similarity Coefficient (DSC), segmentation accuracy, sensitivity, and specificity. First, a single class labeling was used to segment the breast region. Then it was employed as an input for three-class labeling to segment fat, fibroglandular (FGT) tissues, and tumorous lesions. The first architecture has a 0.95 DCS, while the second has a 0.95, 0.83, and 0.41 for fat, FGT, and tumor classes, respectively
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
Implementing decision tree-based algorithms in medical diagnostic decision support systems
As a branch of healthcare, medical diagnosis can be defined as finding the disease based on the signs and symptoms of the patient. To this end, the required information is gathered from different sources like physical examination, medical history and general information of the patient. Development of smart classification models for medical diagnosis is of great interest amongst the researchers. This is mainly owing to the fact that the machine learning and data mining algorithms are capable of detecting the hidden trends between features of a database. Hence, classifying the medical datasets using smart techniques paves the way to design more efficient medical diagnostic decision support systems.
Several databases have been provided in the literature to investigate different aspects of diseases. As an alternative to the available diagnosis tools/methods, this research involves machine learning algorithms called Classification and Regression Tree (CART), Random Forest (RF) and Extremely Randomized Trees or Extra Trees (ET) for the development of classification models that can be implemented in computer-aided diagnosis systems. As a decision tree (DT), CART is fast to create, and it applies to both the quantitative and qualitative data. For classification problems, RF and ET employ a number of weak learners like CART to develop models for classification tasks.
We employed Wisconsin Breast Cancer Database (WBCD), Z-Alizadeh Sani dataset for coronary artery disease (CAD) and the databanks gathered in Ghaem Hospital’s dermatology clinic for the response of patients having common and/or plantar warts to the cryotherapy and/or immunotherapy methods. To classify the breast cancer type based on the WBCD, the RF and ET methods were employed. It was found that the developed RF and ET models forecast the WBCD type with 100% accuracy in all cases. To choose the proper treatment approach for warts as well as the CAD diagnosis, the CART methodology was employed. The findings of the error analysis revealed that the proposed CART models for the applications of interest attain the highest precision and no literature model can rival it. The outcome of this study supports the idea that methods like CART, RF and ET not only improve the diagnosis precision, but also reduce the time and expense needed to reach a diagnosis. However, since these strategies are highly sensitive to the quality and quantity of the introduced data, more extensive databases with a greater number of independent parameters might be required for further practical implications of the developed models