Deep Synthesis of Realistic Medical Images: A Novel Tool in Clinical Research and Training.

Making clinical decisions based on medical images is fundamentally an exercise in statistical decision-making. This is because in this case, the decision-maker must distinguish between image features that are clinically diagnostic (i.e.

Making Expert Decisions Easier to Fathom: On the Explainability of Visual Object Recognition Expertise.

In everyday life, we rely on human experts to make a variety of complex decisions, such as medical diagnoses. These decisions are typically made through some form of weakly guided learning, a form of learning in which decision expertise is gained through labeled examples rather than explicit instructions. Expert decisions can significantly affect people other than the decision-maker (for example, teammates, clients, or patients), but may seem cryptic and mysterious to them.

Invariant object recognition based on extended fragments.

Visual appearance of natural objects is profoundly affected by viewing conditions such as viewpoint and illumination. Human subjects can nevertheless compensate well for variations in these viewing conditions. The strategies that the visual system uses to accomplish this are largely unclear.

What the 'Moonwalk' illusion reveals about the perception of relative depth from motion.

When one visual object moves behind another, the object farther from the viewer is progressively occluded and/or disoccluded by the nearer object. For nearly half a century, this dynamic occlusion cue has been thought to be sufficient by itself for determining the relative depth of the two objects. This view is consistent with the self-evident geometric fact that the surface undergoing dynamic occlusion is always farther from the viewer than the occluding surface.

Unsupervised organization of image collections: taxonomies and beyond.

We introduce a nonparametric Bayesian model, called TAX, which can organize image collections into a tree-shaped taxonomy without supervision. The model is inspired by the Nested Chinese Restaurant Process (NCRP) and associates each image with a path through the taxonomy. Similar images share initial segments of their paths and thus share some aspects of their representation.

Fragment-based learning of visual object categories in non-human primates.

When we perceive a visual object, we implicitly or explicitly associate it with an object category we know. Recent research has shown that the visual system can use local, informative image fragments of a given object, rather than the whole object, to classify it into a familiar category. We have previously reported, using human psychophysical studies, that when subjects learn new object categories using whole objects, they incidentally learn informative fragments, even when not required to do so.

Class-based feature matching across unrestricted transformations.

We develop a novel method for class-based feature matching across large changes in viewing conditions. The method is based on the property that when objects share a similar part, the similarity is preserved across viewing conditions. Given a feature and a training set of object images, we first identify the subset of objects that share this feature.

Fragment-based learning of visual object categories.

When we perceive a visual object, we implicitly or explicitly associate it with a category we know. It is known that the visual system can use local, informative image fragments of a given object, rather than the whole object, to classify it into a familiar category. How we acquire informative fragments has remained unclear.

Recognition invariance obtained by extended and invariant features.

In performing recognition, the visual system shows a remarkable capacity to distinguish between significant and immaterial image changes, to learn from examples to recognize new classes of objects, and to generalize from known to novel objects. Here we focus on one aspect of this problem, the ability to recognize novel objects from different viewing directions. This problem of view-invariant recognition is difficult because the image of an object seen from a novel viewing direction can be substantially different from all previously seen images of the same object.