Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Classifying Breast Histopathology Images with a Ductal Instance-Oriented
  Pipeline   In this study, we propose the Ductal Instance-Oriented Pipeline (DIOP) that
contains a duct-level instance segmentation model, a tissue-level semantic
segmentation model, and three-levels of features for diagnostic classification.
Based on recent advancements in instance segmentation and the Mask R-CNN model,
our duct-level segmenter tries to identify each ductal individual inside a
microscopic image; then, it extracts tissue-level information from the
identified ductal instances. Leveraging three levels of information obtained
from these ductal instances and also the histopathology image, the proposed
DIOP outperforms previous approaches (both feature-based and CNN-based) in all
diagnostic tasks; for the four-way classification task, the DIOP achieves
comparable performance to general pathologists in this unique dataset. The
proposed DIOP only takes a few seconds to run in the inference time, which
could be used interactively on most modern computers. More clinical
explorations are needed to study the robustness and generalizability of this
system in the future.

11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
16
The categories of this paper are: stat
3
The categories of this paper are: physics
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph