A Guided Spatial Transformer Network for Histology Cell Differentiation (Unknown language)

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Identification and counting of cells and mitotic figures is a standard task in diagnostic histopathology. Due to the large overall cell count on histological slides and the potential sparse prevalence of some relevant cell types or mitotic figures, retrieving annotation data for sufficient statistics is a tedious task and prone to a significant error in assessment. Automatic classification and segmentation is a classic task in digital pathology, yet it is not solved to a sufficient degree. We present a novel approach for cell and mitotic figure classification, based on a deep convolutional network with an incorporated Spatial Transformer Network. The network was trained on a novel data set with ten thousand mitotic figures, about ten times more than previous data sets. The algorithm is able to derive the cell class (mitotic tumor cells, non-mitotic tumor cells and granulocytes) and their position within an image. The mean accuracy of the algorithm in a five-fold cross-validation is 91.45 %. In our view, the approach is a promising step into the direction of a more objective and accurate, semi-automatized mitosis counting supporting the pathologist.

Table of contents conference proceedings

The table of contents of the conference proceedings is generated automatically, so it can be incomplete, although all articles are available in the TIB.

1
Protein Tunnel Reprojection for Physico-Chemical Property Analysis
Malzahn, Jan / Kozlíková, Barbora / Ropinski, Timo | 2017
11
Mammogram Classification and Abnormality Detection from Nonlocal Labels using Deep Multiple Instance Neural Network
Choukroun, Yoni / Bakalo, Ran / Ben-Ari, Rami / Akselrod-Ballin, Ayelet / Barkan, Ella / Kisilev, Pavel | 2017
21
A Guided Spatial Transformer Network for Histology Cell Differentiation
Aubreville, Marc / Krappmann, Maximilian / Bertram, Christof / Klopfleisch, Robert / Maier, Andreas | 2017
27
Design Considerations for Immersive Analytics of Bird Movements Obtained by Miniaturised GPS Sensors
Nim, Hieu T. / Sommer, Björn / Klein, Karsten / Flack, Andrea / Safi, Kamran / Nagy, Máté / Fiedler, Wolfgang / Wikelski, Martin / Schreiber, Falk | 2017
33
Watergate: Visual Exploration of Water Trajectories in Protein Dynamics
Vad, Viktor / Byška, Jan / Jurcík, Adam / Viola, Ivan / Gröller, Eduard / Hauser, Helwig / Margues, Sérgio M. / Damborský, Jiří / Kozlíková, Barbora | 2017
43
Visual Analytics of Missing Data in Epidemiological Cohort Studies
Alemzadeh, Shiva / Niemann, Uli / Ittermann, Till / Völzke, Henry / Schneider, Daniel / Spiliopoulou, Myra / Preim, Bernhard | 2017
53
Comparative Visualization for Diffusion Tensor Imaging Group Study at Multiple Levels of Detail
Zhang, Changgong / Höllt, Thomas / Caan, Matthan W. A. / Eisemann, Elmar / Vilanova, Anna | 2017
63
Visualizing and Exploring Dynamic Multichannel EEG Coherence Networks
Ji, Chengtao / Gronde, Jasper J. van de / Maurits, Natasha M. / Roerdink, Jos B. T. M. | 2017
73
HIFUtk: Visual Analytics for High Intensity Focused Ultrasound Simulation
Modena, Daniela / Dijk, Edmond van / Bošnacki, Dragan / Eikelder, Huub M. M. ten / Westenberg, Michel A. | 2017
83
CT-Based Navigation Guidance for Liver Tumor Ablation
Alpers, Julian / Hansen, Christian / Ringe, Kristina / Rieder, Christian | 2017
93
Visual Navigation Support for Liver Applicator Placement using Interactive Map Displays
Hettig, Julian / Mistelbauer, Gabriel / Rieder, Christian / Lawonn, Kai / Hansen, Christian | 2017
103
Application of Image Processing Functions for Brain Tumor Enhancement in Intraoperative Ultrasound Image Data
Chalopin, Claire / Mbuyamba, Elisee Ilunga / Aragon, Jesus Guillermo Cabal / Rodriguez, Juan Carlos Camacho / Arlt, Felix / Cervantes, Juan Gabriel Avina / Meixensberger, Juergen / Lindner, Dirk | 2017
113
MRI Hip Joint Segmentation: A Locally Bhattacharyya Weighted Hybrid 3D Level Set Approach
Pham, Duc Duy / Morariu, Cosmin Adrian / Terheiden, Tobias / Landgraeber, Stefan / Jäger, Marcus / Pauli, Josef | 2017
119
Multi-fiber Estimation and Tractography for Diffusion MRI using mixture of Non-central Wishart Distributions
Shakya, Snehlata / Gu, Xuan / Batool, Nazre / Özarslan, Evren / Knutsson, Hans | 2017
125
Automatic Thrombus Detection in Non-enhanced Computed Tomography Images in Patients With Acute Ischemic Stroke
Löber, Patrick / Stimpel, Bernhard / Syben, Christopher / Maier, Andreas / Ditt, Hendrik / Schramm, Peter / Raczkowski, Boy / Kemmling, André | 2017
131
Maximizing AUC with Deep Learning for Classification of Imbalanced Mammogram Datasets
Sulam, Jeremias / Ben-Ari, Rami / Kisilev, Pavel | 2017
137
A Web-Based Tool for Cardiac Dyssynchrony Assessment on Ultrasound Data
Pezzatini, Daniele / Yagüe, Carlos / Rudenick, Paula / Blat, Josep / Bijnens, Bart / Camara, Oscar | 2017
143
UI-Net: Interactive Artificial Neural Networks for Iterative Image Segmentation Based on a User Model
Amrehn, Mario / Gaube, Sven / Unberath, Mathias / Schebesch, Frank / Horz, Tim / Strumia, Maddalena / Steidl, Stefan / Kowarschik, Markus / Maier, Andreas | 2017
149
Bone Fracture and Lesion Assessment using Shape-Adaptive Unfolding
Martinke, Hannes / Petry, Christian / Großkopf, Stefan / Suehling, Michael / Soza, Grzegorz / Preim, Bernhard / Mistelbauer, Gabriel | 2017
159
Combining Pseudo Chroma Depth Enhancement and Parameter Mapping for Vascular Surface Models
Behrendt, Benjamin / Berg, Philipp / Preim, Bernhard / Saalfeld, Sylvia | 2017
169
Exploration of Interventricular Septum Motion in Multi-Cycle Cardiac MRI
Tautz, Lennart / Hüllebrand, Markus / Steinmetz, Michael / Voit, Dirk / Frahm, Jens / Hennemuth, Anja | 2017
179
Concentric Circle Glyphs for Enhanced Depth-Judgment in Vascular Models
Lichtenberg, Nils / Hansen, Christian / Lawonn, Kai | 2017