181 research outputs found
Revisiting model self-interpretability in a decision-theoretic way for binary medical image classification
Interpretability is highly desired for deep neural network-based classifiers,
especially when addressing high-stake decisions in medical imaging. Commonly
used post-hoc interpretability methods have the limitation that they can
produce plausible but different interpretations of a given model, leading to
ambiguity about which one to choose. To address this problem, a novel
decision-theory-motivated approach is investigated to establish a
self-interpretable model, given a pretrained deep binary black-box medical
image classifier. This approach involves utilizing a self-interpretable
encoder-decoder model in conjunction with a single-layer fully connected
network with unity weights. The model is trained to estimate the test statistic
of the given trained black-box deep binary classifier to maintain a similar
accuracy. The decoder output image, referred to as an equivalency map, is an
image that represents a transformed version of the to-be-classified image that,
when processed by the fixed fully connected layer, produces the same test
statistic value as the original classifier. The equivalency map provides a
visualization of the transformed image features that directly contribute to the
test statistic value and, moreover, permits quantification of their relative
contributions. Unlike the traditional post-hoc interpretability methods, the
proposed method is self-interpretable, quantitative, and fundamentally based on
decision theory. Detailed quantitative and qualitative analysis have been
performed with three different medical image binary classification tasks
Mitigation of artifacts due to isolated acoustic heterogeneities in photoacoustic computed tomography using a variable data truncation-based reconstruction method
Photoacoustic computed tomography (PACT) is an emerging computed imaging
modality that exploits optical contrast and ultrasonic detection principles to
form images of the absorbed optical energy density within tissue. If the object
possesses spatially variant acoustic properties that are unaccounted for by the
reconstruction method, the estimated image can contain distortions. While
reconstruction methods have recently been developed to compensate for this
effect, they generally require the object's acoustic properties to be known a
priori. To circumvent the need for detailed information regarding an object's
acoustic properties, we previously proposed a half-time reconstruction method
for PACT. A half-time reconstruction method estimates the PACT image from a
data set that has been temporally truncated to exclude the data components that
have been strongly aberrated. However, this method can be improved upon when
the approximate sizes and locations of isolated heterogeneous structures, such
as bones or gas pockets, are known. To address this, we investigate PACT
reconstruction methods that are based on a variable data truncation (VDT)
approach. The VDT approach represents a generalization of the half-time
approach, in which the degree of temporal truncation for each measurement is
determined by the distance between the corresponding ultrasonic transducer
location and the nearest known bone or gas void location. Computer-simulated
and experimental data are employed to demonstrate the effectiveness of the
approach in mitigating artifacts due to acoustic heterogeneities
- …