955 research outputs found
Weakly Supervised Universal Fracture Detection in Pelvic X-rays
Hip and pelvic fractures are serious injuries with life-threatening
complications. However, diagnostic errors of fractures in pelvic X-rays (PXRs)
are very common, driving the demand for computer-aided diagnosis (CAD)
solutions. A major challenge lies in the fact that fractures are localized
patterns that require localized analyses. Unfortunately, the PXRs residing in
hospital picture archiving and communication system do not typically specify
region of interests. In this paper, we propose a two-stage hip and pelvic
fracture detection method that executes localized fracture classification using
weakly supervised ROI mining. The first stage uses a large capacity
fully-convolutional network, i.e., deep with high levels of abstraction, in a
multiple instance learning setting to automatically mine probable true positive
and definite hard negative ROIs from the whole PXR in the training data. The
second stage trains a smaller capacity model, i.e., shallower and more
generalizable, with the mined ROIs to perform localized analyses to classify
fractures. During inference, our method detects hip and pelvic fractures in one
pass by chaining the probability outputs of the two stages together. We
evaluate our method on 4 410 PXRs, reporting an area under the ROC curve value
of 0.975, the highest among state-of-the-art fracture detection methods.
Moreover, we show that our two-stage approach can perform comparably to human
physicians (even outperforming emergency physicians and surgeons), in a
preliminary reader study of 23 readers.Comment: MICCAI 2019 (early accept
Anatomy-Aware Siamese Network: Exploiting Semantic Asymmetry for Accurate Pelvic Fracture Detection in X-ray Images
Visual cues of enforcing bilaterally symmetric anatomies as normal findings
are widely used in clinical practice to disambiguate subtle abnormalities from
medical images. So far, inadequate research attention has been received on
effectively emulating this practice in CAD methods. In this work, we exploit
semantic anatomical symmetry or asymmetry analysis in a complex CAD scenario,
i.e., anterior pelvic fracture detection in trauma PXRs, where semantically
pathological (refer to as fracture) and non-pathological (e.g., pose)
asymmetries both occur. Visually subtle yet pathologically critical fracture
sites can be missed even by experienced clinicians, when limited diagnosis time
is permitted in emergency care. We propose a novel fracture detection framework
that builds upon a Siamese network enhanced with a spatial transformer layer to
holistically analyze symmetric image features. Image features are spatially
formatted to encode bilaterally symmetric anatomies. A new contrastive feature
learning component in our Siamese network is designed to optimize the deep
image features being more salient corresponding to the underlying semantic
asymmetries (caused by pelvic fracture occurrences). Our proposed method have
been extensively evaluated on 2,359 PXRs from unique patients (the largest
study to-date), and report an area under ROC curve score of 0.9771. This is the
highest among state-of-the-art fracture detection methods, with improved
clinical indications.Comment: ECCV 2020 (camera-ready
Towards Trainable Saliency Maps in Medical Imaging
While success of Deep Learning (DL) in automated diagnosis can be
transformative to the medicinal practice especially for people with little or
no access to doctors, its widespread acceptability is severely limited by
inherent black-box decision making and unsafe failure modes. While saliency
methods attempt to tackle this problem in non-medical contexts, their apriori
explanations do not transfer well to medical usecases. With this study we
validate a model design element agnostic to both architecture complexity and
model task, and show how introducing this element gives an inherently
self-explanatory model. We compare our results with state of the art
non-trainable saliency maps on RSNA Pneumonia Dataset and demonstrate a much
higher localization efficacy using our adopted technique. We also compare, with
a fully supervised baseline and provide a reasonable alternative to it's high
data labelling overhead. We further investigate the validity of our claims
through qualitative evaluation from an expert reader.Comment: Machine Learning for Health (ML4H) at NeurIPS 2020 - Extended
Abstrac
ENet: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans
Developing an effective liver and liver tumor segmentation model from CT
scans is very important for the success of liver cancer diagnosis, surgical
planning and cancer treatment. In this work, we propose a two-stage framework
for 2D liver and tumor segmentation. The first stage is a coarse liver
segmentation network, while the second stage is an edge enhanced network
(ENet) for more accurate liver and tumor segmentation. ENet explicitly
models complementary objects (liver and tumor) and their edge information
within the network to preserve the organ and lesion boundaries. We introduce an
edge prediction module in ENet and design an edge distance map between
liver and tumor boundaries, which is used as an extra supervision signal to
train the edge enhanced network. We also propose a deep cross feature fusion
module to refine multi-scale features from both objects and their edges.
ENet is more easily and efficiently trained with a small labeled dataset,
and it can be trained/tested on the original 2D CT slices (resolve resampling
error issue in 3D models). The proposed framework has shown superior
performance on both liver and liver tumor segmentation compared to several
state-of-the-art 2D, 3D and 2D/3D hybrid frameworks
Region Proposals for Saliency Map Refinement for Weakly-supervised Disease Localisation and Classification
The deployment of automated systems to diagnose diseases from medical images
is challenged by the requirement to localise the diagnosed diseases to justify
or explain the classification decision. This requirement is hard to fulfil
because most of the training sets available to develop these systems only
contain global annotations, making the localisation of diseases a weakly
supervised approach. The main methods designed for weakly supervised disease
classification and localisation rely on saliency or attention maps that are not
specifically trained for localisation, or on region proposals that can not be
refined to produce accurate detections. In this paper, we introduce a new model
that combines region proposal and saliency detection to overcome both
limitations for weakly supervised disease classification and localisation.
Using the ChestX-ray14 data set, we show that our proposed model establishes
the new state-of-the-art for weakly-supervised disease diagnosis and
localisation.Comment: Early accept at MICCAI 202
Clinically Accurate Chest X-Ray Report Generation
The automatic generation of radiology reports given medical radiographs has
significant potential to operationally and improve clinical patient care. A
number of prior works have focused on this problem, employing advanced methods
from computer vision and natural language generation to produce readable
reports. However, these works often fail to account for the particular nuances
of the radiology domain, and, in particular, the critical importance of
clinical accuracy in the resulting generated reports. In this work, we present
a domain-aware automatic chest X-ray radiology report generation system which
first predicts what topics will be discussed in the report, then conditionally
generates sentences corresponding to these topics. The resulting system is
fine-tuned using reinforcement learning, considering both readability and
clinical accuracy, as assessed by the proposed Clinically Coherent Reward. We
verify this system on two datasets, Open-I and MIMIC-CXR, and demonstrate that
our model offers marked improvements on both language generation metrics and
CheXpert assessed accuracy over a variety of competitive baselines
Learning to Segment Anatomical Structures Accurately from One Exemplar
Accurate segmentation of critical anatomical structures is at the core of
medical image analysis. The main bottleneck lies in gathering the requisite
expert-labeled image annotations in a scalable manner. Methods that permit to
produce accurate anatomical structure segmentation without using a large amount
of fully annotated training images are highly desirable. In this work, we
propose a novel contribution of Contour Transformer Network (CTN), a one-shot
anatomy segmentor including a naturally built-in human-in-the-loop mechanism.
Segmentation is formulated by learning a contour evolution behavior process
based on graph convolutional networks (GCNs). Training of our CTN model
requires only one labeled image exemplar and leverages additional unlabeled
data through newly introduced loss functions that measure the global shape and
appearance consistency of contours. We demonstrate that our one-shot learning
method significantly outperforms non-learning-based methods and performs
competitively to the state-of-the-art fully supervised deep learning
approaches. With minimal human-in-the-loop editing feedback, the segmentation
performance can be further improved and tailored towards the observer desired
outcomes. This can facilitate the clinician designed imaging-based biomarker
assessments (to support personalized quantitative clinical diagnosis) and
outperforms fully supervised baselines.Comment: MICCAI2020 (Early accept
Reliable Liver Fibrosis Assessment from Ultrasound using Global Hetero-Image Fusion and View-Specific Parameterization
Ultrasound (US) is a critical modality for diagnosing liver fibrosis.
Unfortunately, assessment is very subjective, motivating automated approaches.
We introduce a principled deep convolutional neural network (CNN) workflow that
incorporates several innovations. First, to avoid overfitting on non-relevant
image features, we force the network to focus on a clinical region of interest
(ROI), encompassing the liver parenchyma and upper border. Second, we introduce
global heteroimage fusion (GHIF), which allows the CNN to fuse features from
any arbitrary number of images in a study, increasing its versatility and
flexibility. Finally, we use 'style'-based view-specific parameterization (VSP)
to tailor the CNN processing for different viewpoints of the liver, while
keeping the majority of parameters the same across views. Experiments on a
dataset of 610 patient studies (6979 images) demonstrate that our pipeline can
contribute roughly 7% and 22% improvements in partial area under the curve and
recall at 90% precision, respectively, over conventional classifiers,
validating our approach to this crucial problem.Comment: 10 pages, MICCAI 202
Region proposals for saliency map refinement for weakly-supervised disease localisation and classification
First Online: 29 September 2020The deployment of automated systems to diagnose diseases from medical images is challenged by the requirement to localise the diagnosed diseases to justify or explain the classification decision. This requirement is hard to fulfil because most of the training sets available to develop these systems only contain global annotations, making the localisation of diseases a weakly supervised approach. The main methods designed for weakly supervised disease classification and localisation rely on saliency or attention maps that are not specifically trained for localisation, or on region proposals that can not be refined to produce accurate detections. In this paper, we introduce a new model that combines region proposal and saliency detection to overcome both limitations for weakly supervised disease classification and localisation. Using the ChestX-ray14 data set, we show that our proposed model establishes the new state-of-the-art for weakly-supervised disease diagnosis and localisation. We make our code available at https://github.com/renato145/RpSalWeaklyDet.Renato Hermoza, Gabriel Maicas, Jacinto C. Nascimento, Gustavo Carneir
DeepPrognosis: Preoperative Prediction of Pancreatic Cancer Survival and Surgical Margin via Contrast-Enhanced CT Imaging
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers and
carries a dismal prognosis. Surgery remains the best chance of a potential cure
for patients who are eligible for initial resection of PDAC. However, outcomes
vary significantly even among the resected patients of the same stage and
received similar treatments. Accurate preoperative prognosis of resectable
PDACs for personalized treatment is thus highly desired. Nevertheless, there
are no automated methods yet to fully exploit the contrast-enhanced computed
tomography (CE-CT) imaging for PDAC. Tumor attenuation changes across different
CT phases can reflect the tumor internal stromal fractions and vascularization
of individual tumors that may impact the clinical outcomes. In this work, we
propose a novel deep neural network for the survival prediction of resectable
PDAC patients, named as 3D Contrast-Enhanced Convolutional Long Short-Term
Memory network(CE-ConvLSTM), which can derive the tumor attenuation signatures
or patterns from CE-CT imaging studies. We present a multi-task CNN to
accomplish both tasks of outcome and margin prediction where the network
benefits from learning the tumor resection margin related features to improve
survival prediction. The proposed framework can improve the prediction
performances compared with existing state-of-the-art survival analysis
approaches. The tumor signature built from our model has evidently added values
to be combined with the existing clinical staging system.Comment: 11 pages, 3 figures, Early accepted to Medical Image Computing and
Computer Assisted Interventions Conference (MICCAI) 202
- …