An Anatomical Template for the Normalization of Medical Images of Adult Human Hands.

During medical image analysis, it is often useful to align (or 'normalize') a given image of a given body part to a representative standard (or 'template') of that body part. The impact that brain templates have had on the analysis of brain images highlights the importance of templates in general. However, templates for human hands do not exist.

Toward a more nuanced understanding of probability estimation biases.

In real life, we often have to make judgements under uncertainty. One such judgement task is estimating the probability of a given event based on uncertain evidence for the event, such as estimating the chances of actual fire when the fire alarm goes off. On the one hand, previous studies have shown that human subjects often significantly misestimate the probability in such cases.

Satisfaction of Search Can Be Ameliorated by Perceptual Learning: A Proof-of-Principle Study.

When searching a visual image that contains multiple target objects of interest, human subjects often show a satisfaction of search (SOS) effect, whereby if the subjects find one target, they are less likely to find additional targets in the image. Reducing SOS or, equivalently, subsequent search miss (SSM), is of great significance in many real-world situations where it is of paramount importance to find all targets in a given image, not just one. However, studies have shown that even highly trained and experienced subjects, such as expert radiologists, are subject to SOS.

How well do practicing radiologists interpret the results of CAD technology? A quantitative characterization.

Many studies have shown that using a computer-aided detection (CAD) system does not significantly improve diagnostic accuracy in radiology, possibly because radiologists fail to interpret the CAD results properly. We tested this possibility using screening mammography as an illustrative example. We carried out two experiments, one using 28 practicing radiologists, and a second one using 25 non-professional subjects.

Heuristic Vetoing: Top-Down Influences of the Anchoring-and-Adjustment Heuristic Can Override the Bottom-Up Information in Visual Images.

When making decisions under uncertainty, human subjects do not always act as rational decision makers, but often resort to one or more mental "shortcuts", or heuristics, to arrive at a decision. How do such "top-down" processes affect real-world decisions that must take into account empirical, "bottom-up" sensory evidence? Here we use recognition of camouflaged objects by expert viewers as an exemplar case to demonstrate that the effect of heuristics can be so strong as to override the empirical evidence in favor of heuristic information, even though the latter is random. We provided the viewers a random number that we told them was the estimate of a drone reconnaissance system of the probability that the visual image they were about to see contained a camouflaged target.

Biasing Influence of 'Mental Shortcuts' on Diagnostic Decision-Making: Radiologists Can Overlook Breast Cancer in Mammograms When Prior Diagnostic Information Is Available.

When making decisions under uncertainty, people in all walks of life, including highly trained medical professionals, tend to resort to using 'mental shortcuts', or heuristics. Anchoring-and-adjustment (AAA) is a well-known heuristic in which subjects reach a judgment by starting from an initial internal judgment ('anchored position') based on available external information ('anchoring information') and adjusting it until they are satisfied. We studied the effects of the AAA heuristic during diagnostic decision-making in mammography.

Expert camouflage-breakers can accurately localize search targets.

Camouflage-breaking is a special case of visual search where an object of interest, or target, can be hard to distinguish from the background even when in plain view. We have previously shown that naive, non-professional subjects can be trained using a deep learning paradigm to accurately perform a camouflage-breaking task in which they report whether or not a given camouflage scene contains a target. But it remains unclear whether such expert subjects can actually detect the target in this task, or just vaguely sense that the two classes of images are somehow different, without being able to find the target per se.

Deep learning can be used to train naïve, nonprofessional observers to detect diagnostic visual patterns of certain cancers in mammograms: a proof-of-principle study.

The scientific, clinical, and pedagogical significance of devising methodologies to train nonprofessional subjects to recognize diagnostic visual patterns in medical images has been broadly recognized. However, systematic approaches to doing so remain poorly established. Using mammography as an exemplar case, we use a series of experiments to demonstrate that deep learning (DL) techniques can, in principle, be used to train naïve subjects to reliably detect certain diagnostic visual patterns of cancer in medical images.

I SPY WITH MY LITTLE EYE: WHAT VISUAL SEARCH CAN TELL US ABOUT HOW WE SEE THE WORLD.

We have all have experienced the frustration of looking for something we want, only to find a seemingly endless series of things we do not want. This process of looking for an object of interest is called visual search. We perform visual search all the time in everyday life, because the objects we want are almost always surrounded by many other objects.

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.

Neural Mechanisms of High-Level Vision.

The last three decades have seen major strides in our understanding of neural mechanisms of high-level vision, or visual cognition of the world around us. Vision has also served as a model system for the study of brain function. Several broad insights, as yet incomplete, have recently emerged.

Semantic descriptor ranking: a quantitative method for evaluating qualitative verbal reports of visual cognition in the laboratory or the clinic.

For scientific, clinical, and machine learning purposes alike, it is desirable to quantify the verbal reports of high-level visual percepts. Methods to do this simply do not exist at present. Here we propose a novel methodological principle to help fill this gap, and provide empirical evidence designed to serve as the initial "proof" of this principle.

Creating objects and object categories for studying perception and perceptual learning.

In order to quantitatively study object perception, be it perception by biological systems or by machines, one needs to create objects and object categories with precisely definable, preferably naturalistic, properties. Furthermore, for studies on perceptual learning, it is useful to create novel objects and object categories (or object classes) with such properties. Many innovative and useful methods currently exist for creating novel objects and object categories (also see refs.

Learning to break camouflage by learning the background.

How does the visual system recognize a camouflaged object? Obviously, the brain cannot afford to learn all possible camouflaged scenes or target objects. However, it may learn the general statistical properties of backgrounds of interest, which would enable it to break camouflage by comparing the statistics of a background with a target versus the statistics of the same background without a target. To determine whether the brain uses this strategy, we digitally created novel camouflaged scenes that had only the general statistical properties of the background in common.

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.

Object recognition in clutter: cortical responses depend on the type of learning.

Theoretical studies suggest that the visual system uses prior knowledge of visual objects to recognize them in visual clutter, and posit that the strategies for recognizing objects in clutter may differ depending on whether or not the object was learned in clutter to begin with. We tested this hypothesis using functional magnetic resonance imaging (fMRI) of human subjects. We trained subjects to recognize naturalistic, yet novel objects in strong or weak clutter.

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.

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.

A link between visual disambiguation and visual memory.

Sensory information in the retinal image is typically too ambiguous to support visual object recognition by itself. Theories of visual disambiguation posit that to disambiguate, and thus interpret, the incoming images, the visual system must integrate the sensory information with previous knowledge of the visual world. However, the underlying neural mechanisms remain unclear.

How reliable is the pattern adaptation technique? A modeling study.

Upon prolonged viewing of a sinusoidal grating, the visual system is selectively desensitized to the spatial frequency of the grating, while the sensitivity to other spatial frequencies remains largely unaffected. This technique, known as pattern adaptation, has been so central to the psychophysical study of the mechanisms of spatial vision that it is sometimes referred to as the "psychologist's microelectrode." While this approach implicitly assumes that the adaptation behavior of the system is diagnostic of the corresponding underlying neural mechanisms, this assumption has never been explicitly tested.

Preferential responses to occluded objects in the human visual cortex.

How do we see an object when it is partially obstructed from view? The neural mechanisms of this intriguing process are unclear, in part because studies of visual object perception heretofore have largely used stimuli of individual objects, such as faces or common inanimate objects, each presented alone. But in natural images, visual objects are typically occluded by other objects. Computational studies indicate that the perception of an occluded object requires processes that are substantially different from those for an unoccluded object in plain view.