Mitosis Detection in Breast Cancer Histology Images with Multi Column Deep Neural Networks
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1 Mitosis Detection in Breast Cancer Histology Images with Multi Column Deep Neural Networks IDSIA, Lugano, Switzerland Dan C. Cireşan and Alessandro Giusti
2 DNN for Visual Pattern Recognition One of the first to have a DNN implemented on GPU (CUDA), 2009 We applied DNN on a plethora of pattern recognition tasks
3 Why mitosis detection? Mitosis detection is a challenging visual pattern recognition problem No histology or medicine background ICPR2012 & MICCAI2013 competitions: 2012 ICPR Competition: 50 images, 300 mitosis; 17 teams 2013 MICCAI Competition: ~600 images, 1157 mitosis; 14 teams
4 Deep, Convolutional Neural Network
5 D. Ciresan et al. - Mitosis Detection in Breast Cancer Histology Images using Deep Neural Networks, MICCAI 2013
6 Data Description 2048x2048 px (0.5 x 0.5 mm)
7 Method We use a powerful pixel classifier (a Deep Convolutional Neural Network) to detect pixels close to mitosis centroids Input: raw pixel values in a window (no features, no preprocessing) Output: probability of central pixel being close to a mitosis centroid
8 Network Architecture Feature extraction layers 7.5K weights 4.7M connections Classification layers 6.7K (13.4K) weights 6.7K (13.4K) connections
9 Training samples & time 66K positive training samples (all pixels closer than 10 px to a mitosis) 2M negative training samples ICPR months training time for up to 7 epochs on a CPU Or up to 3 days on a GPU
10 Approach Overview
11 Data and nets Training set (263 images with ground truth, coming from 12 patients) We split the training set in two sets T1 (174 images) and T2 (89 images) Initially we trained nets on T1 and validated on T2 Then we trained nets on T1+T2 and applied them to T3 (our submissions) Evaluation set (295 images without ground truth, coming from other 11 patients) Used exclusively for testing Denoted as T3 (ground truth known only by the organizers)
12 Results for net n10 Trained on T1. Results on the validation set (T2) with 8 variations Ground truth is used to decide on which threshold to use when training on T1+T2 T2: (max F1 ~0.64, F1 at 0.4 ~0.6) T3: F1 at We either overfitted on T2, or T2 and T3 are quite different (or both) T2 T2
13 Our submissions n10e06 + n30e05 + n31e02, 8 variations, T1+T2 t=0.45 -> F1-score = t=0.35 -> F1-score = t=0.5 -> F1-score = n10e06, 8 variations, T1, t=0.4 -> F1-score = 0.505
14 Results overview on the evaluation dataset Green: True Positives Red: False Positives Cyan: False Negatives
15 n10 on validation data (T2)
16 Detection results D. Ciresan et al. - Mitosis Detection in Breast Cancer Histology Images using Deep Neural Networks, MICCAI 2013
17 Quantitative Results F1 score: 0.78
18 Assessment of Mitosis Detection Algorithms MICCAI Grand Challenge
19 more training data 2012 ICPR Competition 50 images, 300 mitosis, 17 teams 2013 MICCAI Competition ~600 images, 1157 mitosis, 14 teams test data is more difficult
20 Results
21 % Reannotation experiment Mitoses Non-mitoses histologists reannotated 30% of all our False Positives as actual mitoses they missed during the original annotation 0 IDSIA (N=208) DTU (N=397)
22 How do you compare with machines? A. Giusti at al. - A Comparison of Algorithms and Humans for Mitosis Detection, ISBI 2014
23 IDSIA IDSIA F1 score F1 score Results of Mitosis Detection other entries Competitions ICPR 2012 MICCAI other entries
24 Conclusions No need to extract handcrafted features: the network learns powerful features by itself Big deep nets combining CNN and other ideas are now state of the art for many image classification, detection and segmentation tasks Our DNN won six international competitions DNN can be used for various applications: automotive, biomedicine, detection of defects, document processing, image processing, etc. DNN are already better and much faster than humans on many difficult problems GPUs are essential for training DNN. Testing can be done on CPU. More info:
25 Industry Academic Looking for new projects Unrelated fields: biomedicine, psychology, finance, literature, history Vision for robotics
26 Other projects
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![arxiv: v2 [cs.cv] 8 Jun 2018](/thumbs/87/97185595.jpg "arxiv: v2 [cs.cv] 8 Jun 2018")
Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks Abhijit Guha Roy 1,2, Nassir Navab 2,3, Christian Wachinger 1 arxiv:1803.02579v2 [cs.cv] 8 Jun 2018 1 Artificial Intelligence
Agenda. Digit Classification using CNN Digit Classification using SAE Visualization: Class models, filters and saliency 2 DCT
versus 1 Agenda Deep Learning: Motivation Learning: Backpropagation Deep architectures I: Convolutional Neural Networks (CNN) Deep architectures II: Stacked Auto Encoders (SAE) Caffe Deep Learning Toolbox: