Medical Imaging

The Dataset The paper uses a new dermatologist-labelled dataset of 129,450 clinical images, which also includes 3,374 dermoscopic images. These images come from 18 different clinician-curated, open-access online repositories, as well as from clinical data from Stanford University Medical Center, and belong to 2,032 diseases. This data is split into 127,463 training and validation images, and 1,942 biopsy-labelled test images. ...

This paper proposes a high level latent and shared feature representation from neuroimaging modalities (MRI and PET) via deep learning for the diagnosis of Alzheimer’s Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI). In contrast to the previous works where the multimodal features were combined by concatenating into long vectors or transforming into a high dimensional kernel space, the authors propose using a Deep Boltzmann Machine (DBM) to find a latent hierarchical representation from a 3D patch, and then come up with a method for “a joint feature representation from the paired patches of MRI and PET with a multimodal DBM.” ...

This paper proposes an end-to-end trained fully convolutional neural network model to process 3D image volumes. Unlike previous works that processed the input volumes slice-wise or patch-wise, the authors propose to use volumetric convolutions. Moreover, a new objective function formulated using the Dice coefficient is proposed to be optimized, and the authors demonstrate the fast and superior performance of the algorithm on the segmentation of prostate MRI volumes. ...

Mutual Information Given two discrete random variables $A$ and $B$ with pardinal probability distributions $p_A(a)$ and $p_B(b)$ and joint probability distribution $p_{AB}(a,b)$, the two variables are said to be statistically independent is $p_{AB}(a,b) = p_A(a).p_B(b)$, and are said to be maximally dependent if they are related by a one-to-one mapping $T$ such that $p_A(a) = p_B(T(a)) = p_{AB}(a, T(a))$. The mutual information $I(A,B)$ represents the degree of dependence of A and B ...

Introduction This paper presents an algorithm for non-rigid registration formulated as a discrete labeling problem. The authors note that the two major contemporary works for image registration had inherent flaws - Free-Form Deformation (FFD) based model was crippled by the choice of the set of control points to represent the deformation, while Demons Based Method did not penalize large displacements of pixels and was highly sensitive to local artifacts. The authors demonstrate the proposed algorithm’s superior performance for 2D and 3D registration compared to the two aforementioned algorithms. ...