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In this work, we propose a deep learning system for weakly supervised object detection in digital pathology whole slide images. We designed the system to be organ- and object-agnostic, and to be adapted on-the-fly to detect novel objects based on a few examples provided by the user. We tested our method on detection of healthy glands in colon biopsies and ductal carcinoma in situ (DCIS) of the breast, showing that (1) the same system is capable of adapting to detect requested objects with high accuracy, namely 87% accuracy assessed on 582 detections in colon tissue, and 93% accuracy assessed on 163 DCIS detections in breast tissue; (2) in some settings, the system is capable of retrieving similar cases with little to none false positives (i.e., precision equal to 1.00); (3) the performance of the system can benefit from previously detected objects with high confidence that can be reused in new searches in an iterative fashion.

The number of mitotic figures per tumor area observed in hematoxylin and eosin (H and E) histological tissue sections under light microscopy is an important biomarker for breast cancer prognosis. Whole-slide imaging and computational pathology have enabled the development of automatic mitosis detection algorithms based on convolutional neural networks (CNNs). These models can suffer from high generalization error, i.e. trained networks often underperform on datasets originating from pathology laboratories different than the one that provided the training data, mainly due to the presence of inter-laboratory stain variations. We propose a novel data augmentation strategy that exploits the properties of the H and E color space to simulate a broad range of realistic H and E stain variations. To our best knowledge, this is the first time that data augmentation is performed directly in the H and E color space, instead of RGB. The proposed technique uses color deconvolution to transform RGB images into the H and E color space, modifies the H and E color channels stochastically, and projects them back to RGB space. We trained a CNN-based mitosis detector on homogeneous data from a single institution, and tested its performance on an external, multicenter cohort that contained a wide range of unseen H and E stain variations. We compared CNNs trained with and without the proposed augmentation strategy and observed a significant improvement in performance and robustness to unseen stain variations when the new color augmentation technique was included. In essence, we have shown that CNNs can be made robust to inter-lab stain variation by incorporating extensive stain augmentation techniques.

The detection of cell nuclei plays a key role in various histopathological image analysis problems. Considering the high variability of its applications, we propose a novel generic and trainable detection approach. Adaption to specific nuclei detection tasks is done by providing training samples. A trainable deconvolution and classification algorithm is used to generate a probability map indicating the presence of a nucleus. The map is processed by an extended watershed segmentation step to identify the nuclei positions. We have tested our method on data sets with different stains and target nuclear types. We obtained F1-measures between 0.83 and 0.93.