Browsing by Autor "Mathieu Lamard"

Now showing 1 - 4 of 4

Mapping the retinas of a patient using a mixed set of fundus photographs from both eyes
(2016) Gwenolé Quellec; Mathieu Lamard; Guy Cazuguel; Ali Erginay; Béatrice Cochener
With the increased prevalence of retinal pathologies, automating the detection and progression measurement of these pathologies is becoming more and more relevant. Color fundus photography is the leading modality for assessing retinal pathologies. Because eye fundus cameras have a limited field of view, multiple photographs are taken from each retina during an eye fundus examination. However, operators usually don't indicate which photographs are from the left retina and which ones are from the right retina. This paper presents a novel algorithm that automatically assigns each photograph to one retina and builds a composite image (or "mosaic") per retina, which is expected to push the performance of automated diagnosis forward. The algorithm starts by jointly forming two mosaics, one per retina, using a novel graph theoretic approach. Then, in order to determine which mosaic corresponds to the left retina and which one corresponds to the right retina, two retinal landmarks are detected robustly in each mosaic: the main vessel arch surrounding the macula and the optic disc. The laterality of each mosaic derives from their relative location. Experiments on 2790 manually annotated images validate the very good performance of the proposed framework even for highly pathological images.
Multiple-Instance Learning for Anomaly Detection in Digital Mammography
(Institute of Electrical and Electronics Engineers, 2016) Gwenolé Quellec; Mathieu Lamard; Michel Cozic; Gouenou Coatrieux; Guy Cazuguel
This paper describes a computer-aided detection and diagnosis system for breast cancer, the most common form of cancer among women, using mammography. The system relies on the Multiple-Instance Learning (MIL) paradigm, which has proven useful for medical decision support in previous works from our team. In the proposed framework, breasts are first partitioned adaptively into regions. Then, features derived from the detection of lesions (masses and microcalcifications) as well as textural features, are extracted from each region and combined in order to classify mammography examinations as “normal” or “abnormal”. Whenever an abnormal examination record is detected, the regions that induced that automated diagnosis can be highlighted. Two strategies are evaluated to define this anomaly detector. In a first scenario, manual segmentations of lesions are used to train an SVM that assigns an anomaly index to each region; local anomaly indices are then combined into a global anomaly index. In a second scenario, the local and global anomaly detectors are trained simultaneously, without manual segmentations, using various MIL algorithms (DD, APR, mi-SVM, MI-SVM and MILBoost). Experiments on the DDSM dataset show that the second approach, which is only weakly-supervised, surprisingly outperforms the first approach, even though it is strongly-supervised. This suggests that anomaly detectors can be advantageously trained on large medical image archives, without the need for manual segmentation.
Multiple-instance learning for breast cancer detection in mammograms
(2015) Ruben Sanchez de la Rosa; Mathieu Lamard; Guy Cazuguel; Gouenou Coatrieux; Michel Cozic; Gwenolé Quellec
This paper describes an experimental computer-aided detection and diagnosis system for breast cancer, the most common form of cancer among women, using mammography. The system relies on the Multiple-Instance Learning (MIL) paradigm, which has proven useful for medical decision support in previous works from our team. In the proposed framework, the breasts are first partitioned adaptively into regions. Then, either textural features, or features derived from the detection of masses and microcalcifications, are extracted from each region. Finally, feature vectors extracted from each region are combined using an MIL algorithm (Citation k-NN or mi-Graph), in order to recognize "normal" mammography examinations or to categorize examinations as "normal", "benign" or "cancer". An accuracy of 91.1% (respectively 62.1%) was achieved for normality recognition (respectively three-class categorization) in a subset of 720 mammograms from the DDSM dataset. The paper also discusses future improvements, that will make the most of the MIL paradigm, in order to improve "benign" versus "cancer" discrimination in particular.
Real-time analysis of cataract surgery videos using statistical models
(Springer Science+Business Media, 2017) Katia Charrière; Gwenolé Quellec; Mathieu Lamard; David Martiano; Guy Cazuguel; Gouenou Coatrieux; Béatrice Cochener