Expected exponential loss for gaze-based video and volume ground truth annotation

Many recent machine learning approaches used in medical imaging are highly reliant on large amounts of image and ground truth data. In the context of object segmentation, pixel-wise annotations are extremely expensive to collect, especially in video and 3D volumes. To reduce this annotation burden, we propose a novel framework to allow annotators to simply observe the object to segment and record where they have looked at with a \$200 eye gaze tracker. Our method then estimates pixel-wise probabilities for the presence of the object throughout the sequence from which we train a classifier in semi-supervised setting using a novel Expected Exponential loss function. We show that our framework provides superior performances on a wide range of medical image settings compared to existing strategies and that our method can be combined with current crowd-sourcing paradigms as well.Comment: 9 pages, 5 figues, MICCAI 2017 - LABELS Worksho

Semantic Segmentation of Histopathological Slides for the Classification of Cutaneous Lymphoma and Eczema

Mycosis fungoides (MF) is a rare, potentially life threatening skin disease, which in early stages clinically and histologically strongly resembles Eczema, a very common and benign skin condition. In order to increase the survival rate, one needs to provide the appropriate treatment early on. To this end, one crucial step for specialists is the evaluation of histopathological slides (glass slides), or Whole Slide Images (WSI), of the patients' skin tissue. We introduce a deep learning aided diagnostics tool that brings a two-fold value to the decision process of pathologists. First, our algorithm accurately segments WSI into regions that are relevant for an accurate diagnosis, achieving a Mean-IoU of 69% and a Matthews Correlation score of 83% on a novel dataset. Additionally, we also show that our model is competitive with the state of the art on a reference dataset. Second, using the segmentation map and the original image, we are able to predict if a patient has MF or Eczema. We created two models that can be applied in different stages of the diagnostic pipeline, potentially eliminating life-threatening mistakes. The classification outcome is considerably more interpretable than using only the WSI as the input, since it is also based on the segmentation map. Our segmentation model, which we call EU-Net, extends a classical U-Net with an EfficientNet-B7 encoder which was pre-trained on the Imagenet dataset.Comment: Submitted to https://link.springer.com/chapter/10.1007/978-3-030-52791-4_

Figure Text Extraction in Biomedical Literature

Background: Figures are ubiquitous in biomedical full-text articles, and they represent important biomedical knowledge. However, the sheer volume of biomedical publications has made it necessary to develop computational approaches for accessing figures. Therefore, we are developing the Biomedical Figure Search engin

Computational Intelligence Laboratory,

In this paper we present the state of the art for detecting text in images and video frames and propose an edge-based algorithm for artificial text detection in video frames. First, an edge map is created using the Canny edge detector. Then, morphological filtering is used, based on geometrical constraints, in order to connect the vertical edges and discard false alarms. A connected component analysis is performed to the filtered edge map in order to determine a bounding box for every candidate text area. Finally, horizontal and vertical projections are calculated on the edge map of every box and a threshold is applied, refining the result and splitting text areas in text lines. The whole algorithm is applied in multiresolution fashion to ensure text detection with size variability. Experimental results prove that the method is highly effective and efficient for artificial text detection

A Food Recognition System for Diabetic Patients based on an Optimized Bag of Features Model

Computer vision-based food recognition could be used to estimate a meal's carbohydrate content for diabetic patients. This study proposes a methodology for automatic food recognition, based on the Bag of Features (BoF) model. An extensive technical investigation was conducted for the identification and optimization of the best performing components involved in the BoF architecture, as well as the estimation of the corresponding parameters. For the design and evaluation of the prototype system, a visual dataset with nearly 5,000 food images was created and organized into 11 classes. The optimized system computes dense local features, using the scale-invariant feature transform on the HSV color space, builds a visual dictionary of 10,000 visual words by using the hierarchical k-means clustering and finally classifies the food images with a linear support vector machine classifier. The system achieved classification accuracy of the order of 78%, thus proving the feasibility of the proposed approach in a very challenging image dataset

DETECTING TEXT IN VIDEO FRAMES

In this paper we propose an edge-based algorithm for artificial text detection in video frames. First, an edge map is created using the Canny edge detector. Then, morphological filtering is used, based on geometrical constraints, in order to connect the vertical edges and discard false alarms. A connected component analysis is performed to the filtered edge map in order to determine a bounding box for every candidate text area. Finally, horizontal and vertical projections are calculated on the edge map of every box and a threshold is applied, refining the result and splitting text areas in text lines. The whole algorithm is applied in multiresolution fashion to ensure text detection with size variability. Experimental results prove that the method is highly effective and efficient for artificial text detection

GoCARB

Smartphone-App zur Kohlenhydratberechnung Neue Technologien wie Blutzuckersensoren und moderne Insulinpumpen prägten die Therapie des Typ-1-Diabetes (T1D) in den letzten Jahren in wesentlichem Ausmaß. Smartphones sind aufgrund ihrer rasanten technischen Entwicklung eine weitere Plattform für Applikationen zur Therapieunterstützung bei T1D. GoCARB Hierbei handelt es sich um ein zur Kohlenhydratberechnung entwickeltes System für Personen mit T1D. Die Basis für Endanwender stellt ein Smartphone mit Kamera dar. Zur Berechnung werden 2 mit dem Smartphone aus verschiedenen Winkeln aufgenommene Fotografien einer auf einem Teller angerichteten Mahlzeit benötigt. Zusätzlich ist eine neben dem Teller platzierte Referenzkarte erforderlich. Die Grundlage für die Kohlenhydratberechnung ist ein Computer-Vision-gestütztes Programm, das die Mahlzeiten aufgrund ihrer Farbe und Textur erkennt. Das Volumen der Mahlzeit wird mit Hilfe eines dreidimensional errechneten Modells bestimmt. Durch das Erkennen der Art der Mahlzeiten sowie deren Volumen kann GoCARB den Kohlenhydratanteil unter Einbeziehung von Nährwerttabellen berechnen. Für die Entwicklung des Systems wurde eine Bilddatenbank von mehr als 5000 Mahlzeiten erstellt und genutzt. Resümee Das GoCARB-System befindet sich aktuell in klinischer Evaluierung und ist noch nicht für Patienten verfügbar.Smartphone app for carbohydrate calculation In recent years, new glucose monitoring devices and insulin delivery systems have significantly enhanced diabetes self-management. In parallel, recent advances in smartphone technology has permitted the introduction of a broad spectrum of applications to support diabetic patients in their everyday routine. GoCARB The scope of the GoCARB is the automatic and near real-time estimation of a meal’s carbohydrate content for individuals with type 1 diabetes based on computer vision and smartphone technologies. The user places a credit card-sized reference object next to the meal and acquires two images using a smartphone from different viewing angles. Then, a number of computer vision steps is executed. Initially, the food items on the plate are segmented and recognized while their 3D shape is reconstructed. Based on shape, segmentation results, and reference object, the volume of each item is estimated. Finally, the carbohydrate content is calculated by combining the food type with its volume, and using nutritional databases. For the design and development of GoCARB, a database of more than 5000 meal images has been created and used. Conclusion Currently, the GoCARB system is under clinical evaluation, and therefore not yet accessible for public use

Using Adaptive Run Length Smoothing Algorithm for Accurate Text Localization in Images

Text information in images and videos is frequently a key factor for information indexing and retrieval systems. However, text detection in images is a difficult task since it is often embedded in complex backgrounds. In this paper, we propose an accurate text detection and localization method in images based on stroke information and the Adaptive Run Lenght Smoothing Algorithm. Experimental results show that the proposed approach is accurate, has high recall and is robust to various text sizes, fonts, colors and languages. © 2011 Springer-Verlag.Fil:Rais, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil:Mejail, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina