75 research outputs found
Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classification
Dynamic Textures (DTs) are sequences of images of moving scenes that exhibit
certain stationarity properties in time such as smoke, vegetation and fire. The
analysis of DT is important for recognition, segmentation, synthesis or
retrieval for a range of applications including surveillance, medical imaging
and remote sensing. Deep learning methods have shown impressive results and are
now the new state of the art for a wide range of computer vision tasks
including image and video recognition and segmentation. In particular,
Convolutional Neural Networks (CNNs) have recently proven to be well suited for
texture analysis with a design similar to a filter bank approach. In this
paper, we develop a new approach to DT analysis based on a CNN method applied
on three orthogonal planes x y , xt and y t . We train CNNs on spatial frames
and temporal slices extracted from the DT sequences and combine their outputs
to obtain a competitive DT classifier. Our results on a wide range of commonly
used DT classification benchmark datasets prove the robustness of our approach.
Significant improvement of the state of the art is shown on the larger
datasets.Comment: 19 pages, 10 figure
Regression Concept Vectors for Bidirectional Explanations in Histopathology
Explanations for deep neural network predictions in terms of domain-related
concepts can be valuable in medical applications, where justifications are
important for confidence in the decision-making. In this work, we propose a
methodology to exploit continuous concept measures as Regression Concept
Vectors (RCVs) in the activation space of a layer. The directional derivative
of the decision function along the RCVs represents the network sensitivity to
increasing values of a given concept measure. When applied to breast cancer
grading, nuclei texture emerges as a relevant concept in the detection of tumor
tissue in breast lymph node samples. We evaluate score robustness and
consistency by statistical analysis.Comment: 9 pages, 3 figures, 3 table
Visualizing and Interpreting Feature Reuse of Pretrained CNNs for Histopathology
Reusing the parameters of networks pretrained on large scale datasets of natural images, such as ImageNet, is a common technique in the medical imaging domain. The large variability of objects and classes is, however, drastically reduced in most medical applications where images are dominated by repetitive patterns with, at times, subtle differences between the classes. This paper takes the example of finetuning a pretrained convolutional network on a histopathology task. Because of the reduced visual variability in this application domain, the network mostly learns to detect textures and simple patterns. As a result, the complex structures that maximize the channel activations of deep layers in the pretrained network are not present after finetuning. The learned features seem to be used by the network to spot atypical nuclei in the images, as shown by class activation maps. Finally, texture measures appear discriminative after finetuning, as shown by accurate Regression Concept Vectors
Deep learning for texture and dynamic texture analysis
Texture is a fundamental visual cue in computer vision which provides useful information about image regions. Dynamic Texture (DT) extends the analysis of texture to sequences of moving scenes. Classic approaches to texture and DT analysis are based on shallow hand-crafted descriptors including local binary patterns and filter banks. Deep learning and in particular Convolutional Neural Networks (CNNs) have significantly contributed to the field of computer vision in the last decade. These biologically inspired networks trained with powerful algorithms have largely improved the state of the art in various tasks such as digit, object and face recognition. This thesis explores the use of CNNs in texture and DT analysis, replacing classic hand-crafted filters by deep trainable filters. An introduction to deep learning is provided in the thesis as well as a thorough review of texture and DT analysis methods. While CNNs present interesting features for the analysis of textures such as a dense extraction of filter responses trained end to end, the deepest layers used in the decision rules commonly learn to detect large shapes and image layout instead of local texture patterns. A CNN architecture is therefore adapted to textures by using an orderless pooling of intermediate layers to discard the overall shape analysis, resulting in a reduced computational cost and improved accuracy. An application to biomedical texture images is proposed in which large tissue images are tiled and combined in a recognition scheme. An approach is also proposed for DT recognition using the developed CNNs on three orthogonal planes to combine spatial and temporal analysis. Finally, a fully convolutional network is adapted to texture segmentation based on the same idea of discarding the overall shape and by combining local shallow features with larger and deeper features
Disentangling Neuron Representations with Concept Vectors
Mechanistic interpretability aims to understand how models store
representations by breaking down neural networks into interpretable units.
However, the occurrence of polysemantic neurons, or neurons that respond to
multiple unrelated features, makes interpreting individual neurons challenging.
This has led to the search for meaningful vectors, known as concept vectors, in
activation space instead of individual neurons. The main contribution of this
paper is a method to disentangle polysemantic neurons into concept vectors
encapsulating distinct features. Our method can search for fine-grained
concepts according to the user's desired level of concept separation. The
analysis shows that polysemantic neurons can be disentangled into directions
consisting of linear combinations of neurons. Our evaluations show that the
concept vectors found encode coherent, human-understandable features
Learning Interpretable Microscopic Features of Tumor by Multi-task Adversarial CNNs Improves Generalization
Adopting Convolutional Neural Networks (CNNs) in the daily routine of primary
diagnosis requires not only near-perfect precision, but also a sufficient
degree of generalization to data acquisition shifts and transparency. Existing
CNN models act as black boxes, not ensuring to the physicians that important
diagnostic features are used by the model. Building on top of successfully
existing techniques such as multi-task learning, domain adversarial training
and concept-based interpretability, this paper addresses the challenge of
introducing diagnostic factors in the training objectives. Here we show that
our architecture, by learning end-to-end an uncertainty-based weighting
combination of multi-task and adversarial losses, is encouraged to focus on
pathology features such as density and pleomorphism of nuclei, e.g. variations
in size and appearance, while discarding misleading features such as staining
differences. Our results on breast lymph node tissue show significantly
improved generalization in the detection of tumorous tissue, with best average
AUC 0.89 (0.01) against the baseline AUC 0.86 (0.005). By applying the
interpretability technique of linearly probing intermediate representations, we
also demonstrate that interpretable pathology features such as nuclei density
are learned by the proposed CNN architecture, confirming the increased
transparency of this model. This result is a starting point towards building
interpretable multi-task architectures that are robust to data heterogeneity.
Our code is available at https://bit.ly/356yQ2u.Comment: 21 pages, 4 figure
Solid Spherical Energy (SSE) CNNs for Efficient 3D Medical Image Analysis
Invariance to local rotation, to differentiate from the global rotation of images and objects, is required in various texture analysis problems. It has led to several breakthrough methods such as local binary patterns, maximum response and steerable filterbanks. In particular, textures in medical images often exhibit local structures at arbitrary orientations. Locally Rotation Invariant (LRI) Convolutional Neural Networks (CNN) were recently proposed using 3D steerable filters to combine LRI with Directional Sensitivity (DS). The steerability avoids the expensive cost of convolutions with rotated kernels and comes with a parametric representation that results in a drastic reduction of the number of trainable parameters. Yet, the potential bottleneck (memory and computation) of this approach lies in the necessity to recombine responses for a set of predefined discretized orientations. In this paper, we propose to calculate invariants from the responses to the set of spherical harmonics projected onto 3D kernels in the form of a lightweight Solid Spherical Energy (SSE) CNN. It offers a compromise between the high kernel specificity of the LRI-CNN and a low memory/operations requirement. The computational gain is evaluated on 3D synthetic and pulmonary nodule classification experiments. The performance of the proposed approach is compared with steerable LRI-CNNs and standard 3D CNNs, showing competitive results with the state of the art
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