363 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
Deep Learning Techniques for Video Instance Segmentation: A Survey
Video instance segmentation, also known as multi-object tracking and
segmentation, is an emerging computer vision research area introduced in 2019,
aiming at detecting, segmenting, and tracking instances in videos
simultaneously. By tackling the video instance segmentation tasks through
effective analysis and utilization of visual information in videos, a range of
computer vision-enabled applications (e.g., human action recognition, medical
image processing, autonomous vehicle navigation, surveillance, etc) can be
implemented. As deep-learning techniques take a dominant role in various
computer vision areas, a plethora of deep-learning-based video instance
segmentation schemes have been proposed. This survey offers a multifaceted view
of deep-learning schemes for video instance segmentation, covering various
architectural paradigms, along with comparisons of functional performance,
model complexity, and computational overheads. In addition to the common
architectural designs, auxiliary techniques for improving the performance of
deep-learning models for video instance segmentation are compiled and
discussed. Finally, we discuss a range of major challenges and directions for
further investigations to help advance this promising research field
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