Roto-translation equivariant convolutional networks: Application to histopathology image analysis

Lafarge, Maxime W.; Bekkers, Erik J.; Pluim, Josien P. W.; Duits, Remco; Veta, Mitko

doi:10.1016/j.media.2020.101849

Cited by 62 publications

(47 citation statements)

References 15 publications

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“…We mention the large corpus of literature by Duits et al, see, for example, [ 28 , 29 , 30 ] and the state-of-the-art image inpainting and image recognition algorithms developed by Boscain, Gauthier, et al [ 9 , 31 ]. Some extensions of the CPS model geometry and its applications to other image processing problems can be found in [ 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ].…”

Section: Introductionmentioning

confidence: 99%

A Cortical-Inspired Sub-Riemannian Model for Poggendorff-Type Visual Illusions

et al. 2021

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We consider Wilson-Cowan-type models for the mathematical description of orientation-dependent Poggendorff-like illusions. Our modelling improves two previously proposed cortical-inspired approaches, embedding the sub-Riemannian heat kernel into the neuronal interaction term, in agreement with the intrinsically anisotropic functional architecture of V1 based on both local and lateral connections. For the numerical realisation of both models, we consider standard gradient descent algorithms combined with Fourier-based approaches for the efficient computation of the sub-Laplacian evolution. Our numerical results show that the use of the sub-Riemannian kernel allows us to reproduce numerically visual misperceptions and inpainting-type biases in a stronger way in comparison with the previous approaches.

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Section: Introductionmentioning

confidence: 99%

A Cortical-Inspired Sub-Riemannian Model for Poggendorff-Type Visual Illusions

et al. 2021

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“…Acc. (%) * Teacher (baseline) 80.1 Context-CNN [43] 89.0 SIAT MMLAB [44] 91.8 PCam Dataset AUROC Teacher (baseline) 0.914 VF-CNN [45] 0.951 G-CNN [46], [47] 0.968 DSF-CNN [48] 0.975…”

Section: Lsun Datasetmentioning

confidence: 99%

Toward Scalable and Unified Example-Based Explanation and Outlier Detection

Chong

Cheung

Elovici

et al. 2022

IEEE Trans. on Image Process.

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When neural networks are employed for high-stakes decision-making, it is desirable that they provide explanations for their prediction in order for us to understand the features that have contributed to the decision. At the same time, it is important to flag potential outliers for in-depth verification by domain experts. In this work we propose to unify two differing aspects of explainability with outlier detection. We argue for a broader adoption of prototype-based student networks capable of providing an example-based explanation for their prediction and at the same time identify regions of similarity between the predicted sample and the examples. The examples are real prototypical cases sampled from the training set via our novel iterative prototype replacement algorithm. Furthermore, we propose to use the prototype similarity scores for identifying outliers. We compare performances in terms of the classification, explanation quality, and outlier detection of our proposed network with other baselines. We show that our prototype-based networks beyond similarity kernels deliver meaningful explanations and promising outlier detection results without compromising classification accuracy.

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“…As the name suggests, classification-based models output classification probability maps. Existing works in this sub-category include boundary-based [3], [6]- [10] and connectivity-based [17] methods. Boundary-based methods typically include a boundary detection branch and a semantic segmentation branch [3], [7], [8]; for example, DCAN [3] constructs two separate decoders for boundary detection and semantic segmentation, respectively.…”

Section: Related Workmentioning

confidence: 99%

Boundary-Assisted Region Proposal Networks for Nucleus Segmentation

Chen

Ding

Tao

2020

Lecture Notes in Computer Science

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Nucleus segmentation is a challenging task due to the crowded distribution and blurry boundaries of nuclei. Recent approaches represent nuclei by means of polygons to differentiate between touching and overlapping nuclei and have accordingly achieved promising performance. Each polygon is represented by a set of centroid-toboundary distances, which are in turn predicted by features of the centroid pixel for a single nucleus. However, using the centroid pixel alone does not provide sufficient contextual information for robust prediction. To handle this problem, we propose a Context-aware Polygon Proposal Network (CPP-Net) for nucleus segmentation. First, we sample a point set rather than one single pixel within each cell for distance prediction. This strategy substantially enhances contextual information and thereby improves the robustness of the prediction. Second, we propose a Confidencebased Weighting Module, which adaptively fuses the predictions from the sampled point set. Third, we introduce a novel Shape-Aware Perceptual (SAP) loss that constrains the shape of the predicted polygons. Here, the SAP loss is based on an additional network that is pre-trained by means of mapping the centroid probability map and the pixel-toboundary distance maps to a different nucleus representation. Extensive experiments justify the effectiveness of each component in the proposed CPP-Net. Finally, CPP-Net is found to achieve state-of-the-art performance on three publicly available databases, namely DSB2018, BBBC06, and PanNuke. Code of this paper will be released.

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Roto-translation equivariant convolutional networks: Application to histopathology image analysis

Cited by 62 publications

References 15 publications

A Cortical-Inspired Sub-Riemannian Model for Poggendorff-Type Visual Illusions

A Cortical-Inspired Sub-Riemannian Model for Poggendorff-Type Visual Illusions

Toward Scalable and Unified Example-Based Explanation and Outlier Detection

Boundary-Assisted Region Proposal Networks for Nucleus Segmentation

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