7,660 research outputs found
Pre and Post-hoc Diagnosis and Interpretation of Malignancy from Breast DCE-MRI
We propose a new method for breast cancer screening from DCE-MRI based on a
post-hoc approach that is trained using weakly annotated data (i.e., labels are
available only at the image level without any lesion delineation). Our proposed
post-hoc method automatically diagnosis the whole volume and, for positive
cases, it localizes the malignant lesions that led to such diagnosis.
Conversely, traditional approaches follow a pre-hoc approach that initially
localises suspicious areas that are subsequently classified to establish the
breast malignancy -- this approach is trained using strongly annotated data
(i.e., it needs a delineation and classification of all lesions in an image).
Another goal of this paper is to establish the advantages and disadvantages of
both approaches when applied to breast screening from DCE-MRI. Relying on
experiments on a breast DCE-MRI dataset that contains scans of 117 patients,
our results show that the post-hoc method is more accurate for diagnosing the
whole volume per patient, achieving an AUC of 0.91, while the pre-hoc method
achieves an AUC of 0.81. However, the performance for localising the malignant
lesions remains challenging for the post-hoc method due to the weakly labelled
dataset employed during training.Comment: Submitted to Medical Image Analysi
Building Machines That Learn and Think Like People
Recent progress in artificial intelligence (AI) has renewed interest in
building systems that learn and think like people. Many advances have come from
using deep neural networks trained end-to-end in tasks such as object
recognition, video games, and board games, achieving performance that equals or
even beats humans in some respects. Despite their biological inspiration and
performance achievements, these systems differ from human intelligence in
crucial ways. We review progress in cognitive science suggesting that truly
human-like learning and thinking machines will have to reach beyond current
engineering trends in both what they learn, and how they learn it.
Specifically, we argue that these machines should (a) build causal models of
the world that support explanation and understanding, rather than merely
solving pattern recognition problems; (b) ground learning in intuitive theories
of physics and psychology, to support and enrich the knowledge that is learned;
and (c) harness compositionality and learning-to-learn to rapidly acquire and
generalize knowledge to new tasks and situations. We suggest concrete
challenges and promising routes towards these goals that can combine the
strengths of recent neural network advances with more structured cognitive
models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary
proposals (until Nov. 22, 2016).
https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar
- …