Learning to Detect Blue-White Structures in Dermoscopy Images With Weak Supervision.

We propose a novel approach to identify one of the most significant dermoscopic criteria in the diagnosis of cutaneous Melanoma: the blue-white structure (BWS). In this paper, we achieve this goal in a multiple instance learning (MIL) framework using only image-level labels indicating whether the feature is present or not. To this aim, each image is represented as a bag of (nonoverlapping) regions, where each region may or may not be identified as an instance of BWS. A probabilistic graphical model is trained (in MIL fashion) to predict the bag (image) labels. As output, we predict the classification label for the image (i.e., the presence or absence of BWS in each image) and we also localize the feature in the image. Experiments are conducted on a challenging dataset with results outperforming state-of-the-art techniques, with BWS detection besting competing methods in terms of performance. This study provides an improvement on the scope of modeling for computerized image analysis of skin lesions. In particular, it propounds a framework for identification of dermoscopic local features from weakly labeled data.

Multi-Instance Classification by Max-Margin Training of Cardinality-Based Markov Networks.

We propose a probabilistic graphical framework for multi-instance learning (MIL) based on Markov networks. This framework can deal with different levels of labeling ambiguity (i.e., the portion of positive instances in a bag) in weakly supervised data by parameterizing cardinality potential functions. Consequently, it can be used to encode different cardinality-based multi-instance assumptions, ranging from the standard MIL assumption to more general assumptions. In addition, this framework can be efficiently used for both binary and multiclass classification. To this end, an efficient inference algorithm and a discriminative latent max-margin learning algorithm are introduced to train and test the proposed multi-instance Markov network models. We evaluate the performance of the proposed framework on binary and multi-class MIL benchmark datasets as well as two challenging computer vision tasks: cyclist helmet recognition and human group activity recognition. Experimental results verify that encoding the degree of ambiguity in data can improve classification performance.