The PASCAL Visual Object Classes Challenge

This work provides a unified framework for addressing the problem of visual supervised domain adaptation and generalization with deep models. The main idea is to exploit the Siamese architecture to learn an embedding subspace that is discriminative, and where mapped visual domains are semantically aligned and yet maximally separated. The supervised setting becomes attractive especially when only few target data samples need to be labeled. In this scenario, alignment and separation of semantic probability distributions is difficult because of the lack of data. We found that by reverting to point-wise surrogates of distribution distances and similarities provides an effective solution. In addition, the approach has a high “speed” of adaptation, which requires an extremely low number of labeled target training samples, even one per category can be effective. The approach is extended to domain generalization. For both applications the experiments show very promising results.

Unified Deep Supervised Domain Adaptation and Generalization

Surgical workflow recognition has numerous potential medical applications, such as the automatic indexing of surgical video databases and the optimization of real-time operating room scheduling, among others. As a result, surgical phase recognition has been studied in the context of several kinds of surgeries, such as cataract, neurological, and laparoscopic surgeries. In the literature, two types of features are typically used to perform this task: visual features and tool usage signals. However, the used visual features are mostly handcrafted. Furthermore, the tool usage signals are usually collected via a manual annotation process or by using additional equipment. In this paper, we propose a novel method for phase recognition that uses a convolutional neural network (CNN) to automatically learn features from cholecystectomy videos and that relies uniquely on visual information. In previous studies, it has been shown that the tool usage signals can provide valuable information in performing the phase recognition task. Thus, we present a novel CNN architecture, called EndoNet, that is designed to carry out the phase recognition and tool presence detection tasks in a multi-task manner. To the best of our knowledge, this is the first work proposing to use a CNN for multiple recognition tasks on laparoscopic videos. Experimental comparisons to other methods show that EndoNet yields state-of-the-art results for both tasks.

EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos

Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis

The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved to date; (b) identify common and unique challenges, and strategies that researchers have taken to address these challenges; and (c) identify some of the promising avenues for the future both in terms of applications as well as technical innovations. We introduce the general principles of DL and convolutional neural networks, survey five major areas of application of DL in medical imaging and radiation therapy, identify common themes, discuss methods for dataset expansion, and conclude by summarizing lessons learned, remaining challenges, and future directions.

https://aapm.onlinelibrary.wiley.com/doi/epdf/10.1002/mp.13264

Deep learning in medical imaging and radiation therapy.

Automatic visual detection of instruments in minimally invasive surgery (MIS) can significantly augment the procedure experience for operating clinicians. In this paper, we present a novel technique for detecting surgical instruments by constructing a robust and reliable instrument-part detector. While such detectors are typically slow to use, we introduce a novel early stopping scheme for multiclass ensemble classifiers which acts as a cascade and significantly reduces the computational requirements at test time, ultimately allowing it to run at framerate. We evaluate the effectiveness of our approach on instrument detection in retinal microsurgery and laparoscopic image sequences and demonstrate significant improvements in both accuracy and speed.

/pdf/fast-part-based-classification-for-instrument-detection-in-tuno7l9vvc.pdf

Fast Part-Based Classification for Instrument Detection in Minimally Invasive Surgery

Classification of Aerial Photogrammetric 3D Point Clouds

Efficient and accurate segmentation of cellular structures in microscopic data is an essential task in medical imaging. Many state-of-the-art approaches to image segmentation use structured models whose parameters must be carefully chosen for optimal performance. A popular choice is to learn them using a large-margin framework and more specifically structured support vector machines (SSVM). Although SSVMs are appealing, they suffer from certain limitations. First, they are restricted in practice to linear kernels because the more powerful nonlinear kernels cause the learning to become prohibitively expensive. Second, they require iteratively finding the most violated constraints, which is often intractable for the loopy graphical models used in image segmentation. This requires approximation that can lead to reduced quality of learning. In this paper, we propose three novel techniques to overcome these limitations. We first introduce a method to “kernelize” the features so that a linear SSVM framework can leverage the power of nonlinear kernels without incurring much additional computational cost. Moreover, we employ a working set of constraints to increase the reliability of approximate subgradient methods and introduce a new way to select a suitable step size at each iteration. We demonstrate the strength of our approach on both 2-D and 3-D electron microscopic (EM) image data and show consistent performance improvement over state-of-the-art approaches.

/pdf/learning-structured-models-for-segmentation-of-2-d-and-3-d-1698bccnsr.pdf

Learning Structured Models for Segmentation of 2-D and 3-D Imagery

A common assumption in machine vision is that the training and test samples are drawn from the same distribution. However, there are many problems when this assumption is grossly violated, as in bio-medical applications where different acquisitions can generate drastic variations in the appearance of the data due to changing experimental conditions. This problem is accentuated with 3D data, for which annotation is very time-consuming, limiting the amount of data that can be labeled in new acquisitions for training. In this paper we present a multitask learning algorithm for domain adaptation based on boosting. Unlike previous approaches that learn task-specific decision boundaries, our method learns a single decision boundary in a shared feature space, common to all tasks. We use the boosting-trick to learn a non-linear mapping of the observations in each task, with no need for specific a-priori knowledge of its global analytical form. This yields a more parameter-free domain adaptation approach that successfully leverages learning on new tasks where labeled data is scarce. We evaluate our approach on two challenging bio-medical datasets and achieve a significant improvement over the state of the art.

/pdf/non-linear-domain-adaptation-with-boosting-1xo4n38bc0.pdf

Non-Linear Domain Adaptation with Boosting

In this paper, we improve upon earlier approaches to segmenting mitochondria in Electron Microscopy images by explicitly modeling the double membrane that encloses mitochondria, as well as using features that capture context over an extended neighborhood. We demonstrate that this results in both improved classification accuracy and reduced computational requirements for training.

/pdf/exploiting-enclosing-membranes-and-contextual-cues-for-4jx3b71exr.pdf

Exploiting enclosing membranes and contextual cues for mitochondria segmentation.

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