37 research outputs found
A Multi-resolution Model for Histopathology Image Classification and Localization with Multiple Instance Learning
Histopathological images provide rich information for disease diagnosis.
Large numbers of histopathological images have been digitized into high
resolution whole slide images, opening opportunities in developing
computational image analysis tools to reduce pathologists' workload and
potentially improve inter- and intra- observer agreement. Most previous work on
whole slide image analysis has focused on classification or segmentation of
small pre-selected regions-of-interest, which requires fine-grained annotation
and is non-trivial to extend for large-scale whole slide analysis. In this
paper, we proposed a multi-resolution multiple instance learning model that
leverages saliency maps to detect suspicious regions for fine-grained grade
prediction. Instead of relying on expensive region- or pixel-level annotations,
our model can be trained end-to-end with only slide-level labels. The model is
developed on a large-scale prostate biopsy dataset containing 20,229 slides
from 830 patients. The model achieved 92.7% accuracy, 81.8% Cohen's Kappa for
benign, low grade (i.e. Grade group 1) and high grade (i.e. Grade group >= 2)
prediction, an area under the receiver operating characteristic curve (AUROC)
of 98.2% and an average precision (AP) of 97.4% for differentiating malignant
and benign slides. The model obtained an AUROC of 99.4% and an AP of 99.8% for
cancer detection on an external dataset.Comment: 9 pages, 6 figure
Automated Detection of Cribriform Growth Patterns in Prostate Histology Images
Cribriform growth patterns in prostate carcinoma are associated with poor
prognosis. We aimed to introduce a deep learning method to detect such patterns
automatically. To do so, convolutional neural network was trained to detect
cribriform growth patterns on 128 prostate needle biopsies. Ensemble learning
taking into account other tumor growth patterns during training was used to
cope with heterogeneous and limited tumor tissue occurrences. ROC and FROC
analyses were applied to assess network performance regarding detection of
biopsies harboring cribriform growth pattern. The ROC analysis yielded a mean
area under the curve up to 0.81. FROC analysis demonstrated a sensitivity of
0.9 for regions larger than 0.0150 mm2 with on average 7.5 false positives. To
benchmark method performance for intra-observer annotation variability, false
positive and negative detections were re-evaluated by the pathologists.
Pathologists considered 9% of the false positive regions as cribriform, and 11%
as possibly cribriform; 44% of the false negative regions were not annotated as
cribriform. As a final experiment, the network was also applied on a dataset of
60 biopsy regions annotated by 23 pathologists. With the cut-off reaching
highest sensitivity, all images annotated as cribriform by at least 7/23 of the
pathologists, were all detected as cribriform by the network and 9/60 of the
images were detected as cribriform whereas no pathologist labelled them as
such. In conclusion, the proposed deep learning method has high sensitivity for
detecting cribriform growth patterns at the expense of a limited number of
false positives. It can detect cribriform regions that are labelled as such by
at least a minority of pathologists. Therefore, it could assist clinical
decision making by suggesting suspicious regions.Comment: 15 pages, 6 figure
HookNet: multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images
We propose HookNet, a semantic segmentation model for histopathology
whole-slide images, which combines context and details via multiple branches of
encoder-decoder convolutional neural networks. Concentricpatches at multiple
resolutions with different fields of view are used to feed different branches
of HookNet, and intermediate representations are combined via a hooking
mechanism. We describe a framework to design and train HookNet for achieving
high-resolution semantic segmentation and introduce constraints to guarantee
pixel-wise alignment in feature maps during hooking. We show the advantages of
using HookNet in two histopathology image segmentation tasks where tissue type
prediction accuracy strongly depends on contextual information, namely (1)
multi-class tissue segmentation in breast cancer and, (2) segmentation of
tertiary lymphoid structures and germinal centers in lung cancer. Weshow the
superiority of HookNet when compared with single-resolution U-Net models
working at different resolutions as well as with a recently published
multi-resolution model for histopathology image segmentatio
A Deep Learning Based Approach to Skin Lesion Border Extraction with a Novel Edge Detector in Dermoscopy Images
Lesion border detection is considered a crucial step in diagnosing skin cancer. However, performing such a task automatically is challenging due to the low contrast between the surrounding skin and lesion, ambiguous lesion borders, and the presence of artifacts such as hair. In this paper we propose a two-stage approach for skin lesion border detection: (i) segmenting the skin lesion dermoscopy image using U-Net, and (ii) extracting the edges from the segmented image using a novel approach we call FuzzEdge. The proposed approach is compared with another published skin lesion border detection approach, and the results show that our approach performs better in detecting the main borders of the lesion and is more robust to artifacts that might be present in the image. The approach is also compared with the manual border drawings of a dermatologist, resulting in an average Dice similarity of 87.7%