Deep Learning Under the Microscope: Improving the Interpretability of Medical Imaging Neural Networks

Sampling strategies for bag-of-features image classification

E.Nowak, F.Jurie, and B.Triggs · 2006

Earlier work this paper cites.

Deep inside convolutional networks: Visualising image classification models and saliency maps

Original

K.Simonyan, A.Vedaldi, and A.Zisserman · 2013

Earlier work this paper cites.

Learning deep features for discriminative localization

B.Zhou, A.Khosla, A.Lapedriza, A.Oliva, and A.Torralba · 2016

Earlier work this paper cites.

Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images

K.Sirinukunwattana, S.E.A.Raza, Y.Tsang, D.R.J.Snead, I.A.Cree, and N.M.Rajpoot · 2016

Earlier work this paper cites.

Deep residual learning for image recognition

K.He, X.Zhang, S.Ren, and J.Sun · 2016

Earlier work this paper cites.

Learning deep features for discriminative localization

B.Zhou, A.Khosla, A.Lapedriza, A.Oliva, and A.Torralba · 2016

Earlier work this paper cites.

Grad-cam: Visual explanations from dnns via gradient localization

R.R.Selvaraju, M.Cogswell, A.Das, R.Vedantam, D.Parikh, and D.Batra · 2017

Cited alongside, same era.

Deep multiple instance hashing for scalable medical image retrieval

S.Conjeti, M.Paschali, A.Katouzian, and N.Navab · 2017

Cited alongside, same era.

Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer

B.E.Bejnordi, M.Veta, P.J.v.Diest, B.v.Ginneken, N.Karssemeijer, G.Litjens, J.v.d.Laak, , and the CAMELYON16 Consortium · 2017

Cited alongside, same era.

Densely connected convolutional networks

G.Huang, Z.Liu, L.V.D.Maaten, and K.Q.Weinberger · 2017

Cited alongside, same era.

Methods for interpreting and understanding deep neural networks

G.Montavon, W.Samek, and K.R.Müller · 2018

Cited alongside, same era.

Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (TCAV)

Tell me where to look: Guided attention inference network

K.Li, Z.Wu, K.C.Peng, J.Ernst, and Y.Fu · 2018

Later among the works it cites.

Multiple instance learning: A survey of problem characteristics and applications

M.A.Carbonneau, V.Cheplygina, E.Granger, and G.Gagnon · 2018

Later among the works it cites.

Attention-based deep multiple instance learning

M.Ilse, J.M.Tomczak, and M.Welling · 2018

Later among the works it cites.

A survey on deep neural network-based image captioning

X.Liu, Q.Xu, and N.Wang · 2019

Closest in time.

Approximating CNNs with bag-of-local-features models works surprisingly well on imagenet

W.Brendel and M.Bethge · 2019

Closest in time.