Abscess Drainage/Biopsy

Image-guided percutaneous drainage of abdominal collections and image-guided percutaneous biopsy of abdominal masses are two of the most common procedures performed by interventional radiologists. These procedures have almost entirely replaced surgical methods due to their minimally invasive nature and high rate of success. Percutaneous drainage is a very effective treatment for various types of collections, and percutaneous biopsies are a vital step in the diagnosis of several malignant and benign diseases. Effectiveness in performing either procedure requires technical skills, image interpretation skills, and strong clinical skills. The following sections will provide an overview of these two ubiquitous procedures

Higher order contractive auto-encoder

Abstract. We propose a novel regularizer when training an auto-encoder for unsupervised feature extraction. We explicitly encourage the latent representation to contract the input space by regularizing the norm of the Jacobian (analytically) and the Hessian (stochastically) of the encoder’s output with respect to its input, at the training points. While the penalty on the Jacobian’s norm ensures robustness to tiny corruption of samples in the input space, constraining the norm of the Hessian extends this robustness when moving further away from the sample. From a manifold learning perspective, balancing this regularization with the auto-encoder’s reconstruction objective yields a representation that varies most when moving along the data manifold in input space, and is most insensitive in directions orthogonal to the manifold. The second order regularization, using the Hessian, penalizes curvature, and thus favors smooth manifold. We show that our proposed technique, while remaining computationally efficient, yields representations that are significantly better suited for initializing deep architectures than previously proposed approaches, beating state-of-the-art performance on a number of datasets