1.5k citations · 2.4k across the 18 of their papers we have counts for
18 papers
Deep conditional transformation models for survival analysis
Gabriele Campanella, Lucas Kook, Ida Häggström +2
An every increasing number of clinical trials features a time-to-event outcome and records non-tabular patient data, such as magnetic resonance imaging or text data in the form of…
Deep Learning-Based Objective and Reproducible Osteosarcoma Chemotherapy Response Assessment and Outcome Prediction
David Joon Ho, Narasimhan P. Agaram, Marc-Henri Jean +8
Osteosarcoma is the most common primary bone cancer whose standard treatment includes pre-operative chemotherapy followed by resection. Chemotherapy response is used for predicting…
Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation
David Joon Ho, M. Herman Chui, Chad M. Vanderbilt +5
Deep learning has been widely used to analyze digitized hematoxylin and eosin (H&E)-stained histopathology whole slide images. Automated cancer segmentation using deep learning can…
EPIC-Survival: End-to-end Part Inferred Clustering for Survival Analysis, Featuring Prognostic Stratification Boosting
Hassan Muhammad, Chensu Xie, Carlie S. Sigel +5
Histopathology-based survival modelling has two major hurdles. Firstly, a well-performing survival model has minimal clinical application if it does not contribute to the stratific…
Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment
David Joon Ho, Narasimhan P. Agaram, Peter J. Schueffler +4
Osteosarcoma is the most common malignant primary bone tumor. Standard treatment includes pre-operative chemotherapy followed by surgical resection. The response to treatment as me…
Deep Multi-Magnification Networks for Multi-Class Breast Cancer Image Segmentation
David Joon Ho, Dig V. K. Yarlagadda, Timothy M. D'Alfonso +6
Pathologic analysis of surgical excision specimens for breast carcinoma is important to evaluate the completeness of surgical excision and has implications for future treatment. Th…
Towards Unsupervised Cancer Subtyping: Predicting Prognosis Using A Histologic Visual Dictionary
Hassan Muhammad, Carlie S. Sigel, Gabriele Campanella +9
Unlike common cancers, such as those of the prostate and breast, tumor grading in rare cancers is difficult and largely undefined because of small sample sizes, the sheer volume of…
Terabyte-scale Deep Multiple Instance Learning for Classification and Localization in Pathology
Gabriele Campanella, Vitor Werneck Krauss Silva, Thomas J. Fuchs
In the field of computational pathology, the use of decision support systems powered by state-of-the-art deep learning solutions has been hampered by the lack of large labeled data…
DeepPET: A deep encoder-decoder network for directly solving the PET reconstruction inverse problem
Ida Häggström, C. Ross Schmidtlein, Gabriele Campanella +1
Positron emission tomography (PET) is a cornerstone of modern radiology. The ability to detect cancer and metastases in whole body scans fundamentally changed cancer diagnosis and…
Mitochondria-based Renal Cell Carcinoma Subtyping: Learning from Deep vs. Flat Feature Representations
Peter J. Schüffler, Judy Sarungbam, Hassan Muhammad +3
Accurate subtyping of renal cell carcinoma (RCC) is of crucial importance for understanding disease progression and for making informed treatment decisions. New discoveries of sign…
Multi-Organ Cancer Classification and Survival Analysis
Stefan Bauer, Nicolas Carion, Peter Schüffler +3
Accurate and robust cell nuclei classification is the cornerstone for a wider range of tasks in digital and Computational Pathology. However, most machine learning systems require…
Real-Time Data Mining of Massive Data Streams from Synoptic Sky Surveys
S. G. Djorgovski, M. J. Graham, C. Donalek +4
The nature of scientific and technological data collection is evolving rapidly: data volumes and rates grow exponentially, with increasing complexity and information content, and t…
Computational Pathology: Challenges and Promises for Tissue Analysis
Thomas J. Fuchs, Joachim M. Buhmann
The histological assessment of human tissue has emerged as the key challenge for detection and treatment of cancer. A plethora of different data sources ranging from tissue microar…
Understanding Neural Networks Through Deep Visualization
Jason Yosinski, Jeff Clune, Anh Nguyen +2
Recent years have produced great advances in training large, deep neural networks (DNNs), including notable successes in training convolutional neural networks (convnets) to recogn…
Boosting Convolutional Features for Robust Object Proposals
Nikolaos Karianakis, Thomas J. Fuchs, Stefano Soatto
Deep Convolutional Neural Networks (CNNs) have demonstrated excellent performance in image classification, but still show room for improvement in object-detection tasks with many c…
Automated Real-Time Classification and Decision Making in Massive Data Streams from Synoptic Sky Surveys
S. G. Djorgovski, A. A. Mahabal, C. Donalek +4
The nature of scientific and technological data collection is evolving rapidly: data volumes and rates grow exponentially, with increasing complexity and information content, and t…
Early Recognition of Human Activities from First-Person Videos Using Onset Representations
M. S. Ryoo, Thomas J. Fuchs, Lu Xia +2
In this paper, we propose a methodology for early recognition of human activities from videos taken with a first-person viewpoint. Early recognition, which is also known as activit…
Feature Selection Strategies for Classifying High Dimensional Astronomical Data Sets
Ciro Donalek, Arun Kumar A., S. G. Djorgovski +7
The amount of collected data in many scientific fields is increasing, all of them requiring a common task: extract knowledge from massive, multi parametric data sets, as rapidly an…