Training a deep learning model with a large annotated dataset is still a dominant paradigm in automatic whole slide images (WSIs) processing for digital pathology. However, obtaining manual annotations is a labor-intensive task, and an error-prone to inter and intra-observer variability. In this study, we offer an online deep learning-based clustering workflow for annotating and analysis of different types of tissues from histopathology images. Inspired by learning and optimal transport theory, our proposed model consists of two stages. In the first stage, our model learns tissue-specific discriminative representations by contrasting the features in the latent space at two levels, the instance- and the clusterlevel. This is done by maximizing the similarities of the projections of positive pairs (views of the same image) while minimizing those of negative ones (views of the rest of the images). In the second stage, our framework extends the standard cross-entropy minimization to an optimal transport problem and solves it using the Sinkhorn-Knopp algorithm to produce the cluster assignments. Moreover, our proposed method enforces consistency between the produced assignments obtained from views of the same image. Our framework was evaluated on three common histopathological datasets: NCT-CRC, LC2500, and Kather STAD. Experiments show that our proposed framework can identify different tissues in annotation-free conditions with competitive results. It achieved an accuracy of 0.9364 in human lung patched WSIs and 0.8464 in images of human colorectal tissues outperforming state of the arts contrastive-based methods.
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JECL: Joint Embedding and Cluster Learning for Image-Text Pairs
We propose JECL, a method for clustering image-caption pairs by training parallel encoders with regularized clustering and alignment objectives, simultaneously learning both representations and cluster assignments. These image-caption pairs arise frequently in high-value applications where structured training data is expensive to produce, but free-text descriptions are common. JECL trains by minimizing the Kullback-Leibler divergence between the distribution of the images and text to that of a combined joint target distribution and optimizing the Jensen-Shannon divergence between the soft cluster assignments of the images and text. Regularizers are also applied to JECL to prevent trivial solutions. Experiments show that JECL outperforms both single-view and multi-view methods on large benchmark image-caption datasets, and is remarkably robust to missing captions and varying data sizes.
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- PAR ID:
- 10293147
- Date Published:
- Journal Name:
- 2020 25th International Conference on Pattern Recognition (ICPR)
- Page Range / eLocation ID:
- 8344 to 8351
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICPR48806.2021.9412667
