Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies

My work on deep learning based analysis of breast tumor stroma in collaboration with Harvard Medical school, NCI, and Mayo Clinic. •


Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer

















In this research, we investigated and compared the discriminative accuracy of deep learning algorithms with the diagnoses of pathologists in detecting lymph node metastases in tissue sections of women with breast cancer.

Our work has been published in the Journal of the American Medical Association and is available here.


Our work has attracted media attention and has so far obtained a higher Altmetric attention score than 99% of its peers.



Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images

In this paper, we present context-aware stacked convolutional neural networks (CNN) for classification of breast WSIs into normal/benign, ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC). The Preprint version of this paper can be found here: •




Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images

This is the first study showing the discriminating power of stromal properties of the tissue for diagnosing breast cancer. The corresponding manuscript accepted for publication in proceedings of IEEE ISBI can be found here •








Stain specific standardization of whole-slide histopathological images

Our paper presents a fully automated algorithm for standardization of whole-slide histopathological images to reduce the effect of these variations. The corresponding manuscript has been published in IEEE transactions on medical imaging 2016 (IEEE TMI) • Source code | Windows | Docker



Automated Detection of DCIS in Whole-Slide H&E Stained Breast Histopathology Images

This paper presents and evaluates a fully automatic method based on spatial clustering of superpixels for detection of ductal carcinoma in situ (DCIS) in digitized hematoxylin and eosin (H&E) stained histopathological slides of breast tissue. The corresponding manuscript has been published in IEEE transactions on medical imaging 2016 (IEEE TMI) •