deep-learning techniques for some time to come.
Deep learning, in contrast, is already showing
progress in automated unsupervised analysis of
mammograms (Suzuki, 2016; Wang, 2016),
Three deep-conventional learning fusion
examples have already appeared in the field of
automated histopathology. Zhong and colleagues
fused information from deep learning and
conventional learning (Zhong, 2017). In comparing
multiple machine learning strategies, it was found
that the combination of biologically inspired
conventional cellular morphology features (CMF)
and predictive sparse decomposition deep learning
features provided the best separation of benign and
malignant histology sections (Zhong, 2017). The
deep learning arm used a pre-trained AlexNet
network (transfer learning). The conventional arm
used cellular morphology features, which include
nuclear size, aspect ratio, and mean nuclear gradient.
The researchers concluded that both CMF features
and sparse decomposition deep learning features
encode meaningful biological patterns.
Wang and colleagues were able to detect mitoses
in breast cancer histopathology images by using the
combined manually-tuned CMF data and convolu-
tional neural net features (Wang, 2014).
Arevalo and colleagues added an interpretable
layer they called “digital staining,” to aid in their
deep learning approach to classification of basal cell
carcinoma (Arevalo, 2015). Of interest, the
handcrafted layer finds the area of importance,
reproducing the high-level search strategy of the
expert pathologist.
7 CONCLUSION
Deep learning has shown its ability to solve, with a
high degree of accuracy, rather complex problems.
But conventional machine learning and image
processing techniques should not be totally
discounted. Deep learning’s ability does not come
without a cost: time and dataset requirements. With
very large datasets, deep learning is already the
preferred method to use, but may not be ideal for
smaller datasets. Although conventional machine
learning and image processing may be more labor
intensive, they provide a tool for situations lacking
sufficient data, despite augmentation techniques. We
offer a conjectural model which shows advantages
for conventional learning techniques for small
datasets; advantages shift to deep learning after
some dataset size. We call this dataset size the
“learning equilibrium” (LE). It would be interesting
to study how many images are needed for deep
learning approaches to be effective in different
applications. Another topic for future research is to
determine the characteristics that make one
application require a larger dataset than another. We
may consider the dataset size at the LE to be an
application-specific trade-off; for applications in
which conventional models are effective, the LE
point will be larger.
In some applications, such as histopathology, and
related applications such as dermoscopy, biological
constraints are best modeled by manually-tuned
features. Therefore in these applications especially,
the LE dataset size is large. In these applications
there is still room for familiar computer vision
techniques in the novel world of deep learning.
REFERENCES
LeCun Y., Bengio Y., Hinton G. Deep learning. Nature.
2015 May 28;521(7553):436-44.
Bengio Y., Courville A., Vincent P. Representation
learning: a review and new perspectives. IEEE Trans
Pattern Anal Mach Intell. 2013 Aug; 35(8):1798-828.
Goodfellow I., Bengio Y., Courville A. Deep Learning.
Cambridge MA, MIT Press, 2016.
Allen, Kate. "How a Toronto Professor's Research
Revolutionized Artificial Intelligence | Toronto Star."
Thestar.com. N.p., 17 Apr. 2015. Web. 09 Jan. 2017.
LeCun Y., Bottou L., Bengio Y., Haffner, P., Gradient-
Based Learning Applied to Document Recognition,
Proceedings of the IEEE, 86(11):2278-2324, Nov.
1998.
Goodfellow I. J., Erhan D., Luc Carrier P., et al.
Challenges in representation learning: a report on three
machine learning contests. Neural Netw. 2015 Apr;
64:59-63. PMID: 25613956
Valiant L. "A theory of the learnable", Commun. ACM,
vol. 27, pp. 1134-1142, Nov. 1984.
Brown L. “Deep learning with GPUs”, Larry Brown
Ph.D., Johns Hopkins University, June 2015
http://www.nvidia.com/content/events/geoInt2015/LB
rown_DL.pdf accessed November 28, 2016
"ImageNet", Image-net.org, 2016. [Online]. Available:
http://image-net.org/. [Accessed: 29- Nov- 2016].
"ISIC Archive", Isic-archive.com, 2016. [Online].
Available: https://isic-archive.com. [Accessed: 29-
Nov- 2016].
Krieger N., Hiatt R. A., Sagebiel R. W., Clark W. H.,
Mihm M.C. Inter-observer variability among
pathologists' evaluation of malignant melanoma:
effects upon an analytic study. J Clin Epidemiol. 1994
Aug; 47(8):897-902.
Guo P., Banerjee K., Stanley R., Long R., Antani S.,
Thoma G., Zuna R., Frazier S., Moss R., Stoecker W.
Nuclei-Based Features for Uterine Cervical Cancer
Histology Image Analysis with Fusion-based