Learning to detect chest radiographs containing lung nodules using visual attention networks

Machine learning approaches hold great potential for the automated detection of lung nodules in chest radiographs, but training the algorithms requires vary large amounts of manually annotated images, which are difficult to obtain. Weak labels indicating whether a radiograph is likely to contain pulmonary nodules are typically easier to obtain at scale by parsing historical free-text radiological reports associated to the radiographs. Using a repositotory of over 700,000 chest radiographs, in this study we demonstrate that promising nodule detection performance can be achieved using weak labels through convolutional neural networks for radiograph classification. We propose two network architectures for the classification of images likely to contain pulmonary nodules using both weak labels and manually-delineated bounding boxes, when these are available. Annotated nodules are used at training time to deliver a visual attention mechanism informing the model about its localisation performance. The first architecture extracts saliency maps from high-level convolutional layers and compares the estimated position of a nodule against the ground truth, when this is available. A corresponding localisation error is then back-propagated along with the softmax classification error. The second approach consists of a recurrent attention model that learns to observe a short sequence of smaller image portions through reinforcement learning. When a nodule annotation is available at training time, the reward function is modified accordingly so that exploring portions of the radiographs away from a nodule incurs a larger penalty. Our empirical results demonstrate the potential advantages of these architectures in comparison to competing methodologies

Μεθοδολογία Αποκριτικών Επιφανειών-Σχεδιασμοί Υποσυνόλου

60 σ.Η μεθοδολογία των αποκριρικών επιφανειών περιλαμβάνει τεχνικές των μαθηματικών και της στατιστικής, χρήσιμες για την μοντελοποίηση και την ανάλυση προβλημάτων, στα οποία η απόκριση επηρεάζεται απο ορισμένες μεταβλητές.The methodology of response surface uses mathematical and statistical instruments in order to descrise the response.Πέτρος-Παύλος Δ. Υψηλάντη

Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks.

Imaging of cancer with 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) has become a standard component of diagnosis and staging in oncology, and is becoming more important as a quantitative monitor of individual response to therapy. In this article we investigate the challenging problem of predicting a patient's response to neoadjuvant chemotherapy from a single 18F-FDG PET scan taken prior to treatment. We take a "radiomics" approach whereby a large amount of quantitative features is automatically extracted from pretherapy PET images in order to build a comprehensive quantification of the tumor phenotype. While the dominant methodology relies on hand-crafted texture features, we explore the potential of automatically learning low- to high-level features directly from PET scans. We report on a study that compares the performance of two competing radiomics strategies: an approach based on state-of-the-art statistical classifiers using over 100 quantitative imaging descriptors, including texture features as well as standardized uptake values, and a convolutional neural network, 3S-CNN, trained directly from PET scans by taking sets of adjacent intra-tumor slices. Our experimental results, based on a sample of 107 patients with esophageal cancer, provide initial evidence that convolutional neural networks have the potential to extract PET imaging representations that are highly predictive of response to therapy. On this dataset, 3S-CNN achieves an average 80.7% sensitivity and 81.6% specificity in predicting non-responders, and outperforms other competing predictive models

Prediction of Outcome in Acute Lower Gastrointestinal Bleeding Using Gradient Boosting

There are no widely used models in clinical care to predict outcome in acute lower gastro-intestinal bleeding (ALGIB). If available these could help triage patients at presentation to appropriate levels of care/intervention and improve medical resource utilisation. We aimed to apply a state-of-the-art machine learning classifier, gradient boosting (GB), to predict outcome in ALGIB using non-endoscopic measurements as predictors.Non-endoscopic variables from patients with ALGIB attending the emergency departments of two teaching hospitals were analysed retrospectively for training/internal validation (n=170) and external validation (n=130) of the GB model. The performance of the GB algorithm in predicting recurrent bleeding, clinical intervention and severe bleeding was compared to a multiple logic regression (MLR) model and two published MLR-based prediction algorithms (BLEED and Strate prediction rule).The GB algorithm had the best negative predictive values for the chosen outcomes (>88%). On internal validation the accuracy of the GB algorithm for predicting recurrent bleeding, therapeutic intervention and severe bleeding were (88%, 88% and 78% respectively) and superior to the BLEED classification (64%, 68% and 63%), Strate prediction rule (78%, 78%, 67%) and conventional MLR (74%, 74% 62%). On external validation the accuracy was similar to conventional MLR for recurrent bleeding (88% vs. 83%) and therapeutic intervention (91% vs. 87%) but superior for severe bleeding (83% vs. 71%).The gradient boosting algorithm accurately predicts outcome in patients with acute lower gastrointestinal bleeding and outperforms multiple logistic regression based models. These may be useful for risk stratification of patients on presentation to the emergency department

Classification results: each figure is the average of three independent experiments using different training and test datasets.

<p>Classification results: each figure is the average of three independent experiments using different training and test datasets.</p

Examples of feature maps in the first and last max-pooling layers <b>V</b>⁽¹⁾<b>,</b><b>V</b>⁽⁴⁾ of the CNN architecture.

<p>The feature maps illustrate how a specific triplet is represented in the first and last max-pooling layers.</p

Distributions of axial ¹⁸F-FDG PET intra slices extracted from the 3D tumor volume of non-responders and responders.

<p>Distributions of axial ¹⁸F-FDG PET intra slices extracted from the 3D tumor volume of non-responders and responders.</p

Kaplan-Meier plot showing the survival rates of responders and non-responders.

<p>Kaplan-Meier plot showing the survival rates of responders and non-responders.</p

¹⁸F-FDG PET ROIs of a specific tumor <i>i</i> after segmentation embedded into larger square background of standard size of 100 × 100 pixels.

<p>Each enlarged slice is denoted by <b>x</b>_<i>i,j</i> and each set of three spatially adjacent enlarged slides is denoted by <b>z</b>_<i>i,k</i>, where <i>j</i> and <i>k</i> represent the slices and triplets of the specific tumor <i>i</i>. In this example only 3 triplets, from the 5 available slices can be formed, so <i>k</i> = 1,2,3.</p

Top ten variable importance using gradient boosting models.

<p>Top ten variable importance using gradient boosting models.</p