Medical Multimedia Information Systems (ACMMM17 Tutorial)

Medical Multimedia
Information Systems
Klaus Schoeffmann1, Bernd Münzer1, Pål Halvorsen2, Michael Riegler2
1 Institute of Information Technology
Klagenfurt University, Austria
2 Simula Research Laboratory
Norway

• Introduction & Overview
• Multimedia Data in Medicine
• Characteristics of Endoscopic Video
• Different Fields and Communities
• Application 1: Post-Procedural Usage of Surgery Videos
• Domain-Specific Storage for long-term Archiving
• Video Content Analysis
• Visualization, Interaction & Annotation
• Application 2: Diagnostic Decision Support
• Knowledge transfer
• Analysis
• Feedback
• Conclusions & Outlook
Agenda
ACM Multimedia 2017 Tutorial Medical Multimedia Information Systems (MMIS) 2

Introduction

Inspections and intervention produce many kinds of data
• Medical text
• OR reports, Patient records…
• Sensor signals
• ECG, EEG, vital signs
• Medical images (radiology)
• Ultrasound, x-ray
• CT, MRI, PET, …
• Medical video
• Open surgery
• Microscopic surgery
• Endoscopic inspections
• Endoscopic surgery
Multimedia Data in Medicine
Communities:
• Signal Processing
• Medical Imaging
• Computer-Assisted
Surgery / Robotics
• Multimedia
„Human EEG without alpha-rhythm“ by Andrii Cherninskyi / CC BY-SA
„Pankreatitis“ by Hellerhoff/ CC BY-SA„Ultrasound“, Public Domain

• Traditional open surgery ?
• Minimally invasive interventions
• Reduced trauma for patient
• Inherently available video signal
• Useful for documentation
• Microscopic surgery
Video Data Sources in Medicine
„Laparoscopy“, Public Domain

„Kussmaul Gastroscopy“, Public Domain
Diagnostic Endoscopy
• Diagnosis / Inspections
• Gastroenterology (colonoscopy, gastroscopy)
• Bronchoscopy
• Hysteroscopy
• …
• Flexible endoscope
• Natural orifices
• WCE (Wireless capsule endoscopy)
„Colonoscopy“, Public Domain
„Kolon transversum“ by J.Guntau / CC BY-SA

Therapeutic Endoscopy
• Therapy / Surgery
• Laparoscopy
• Cholecystectomy
• Gynecological Surgery
• Urological Surgery
• …
• Arthroscopy
• …
• Rigid endoscope
• Small Incisions „Laparoscopy“ by BruceBlaus / CC BY
„Arthroscopy“, Public Domain

Endoscopic Video Examples

Domain-specific Characteristics & Challenges
• Full HD or 4K (even stereo 3D)
• Single shot recordings
• Up to multiple hours
• Homogenous color distribution
• Visually very similar content
• Circular content area
• Restricted motion
• Geometric distortion
• Specular reflections
• Occlusions
• Smoke
• Noise, motion blur, blood, flying particles

Research Fields and Communities

Overview
Münzer, Bernd, Klaus Schoeffmann, and Laszlo Böszörmenyi. "Content-based processing and analysis of endoscopic
images and videos: A survey." Multimedia Tools and Applications (2017): 1-40.

Pre-Processing
• Image Enhancement
• Contrast enhancement, color misalignment
correction…
• Camera calibration and distortion correction
• Specular reflection removal
• Comb structure removal & super resolution
• …
• Information Filtering
• Frame Filtering
• Image Segmentation
T. Stehle. Removal of specular reflections in endoscopic images. Acta
Polytechnica: Journal of Advanced Engineering, 46(4):32–36, 2006.
J. Barreto, J. Roquette, P. Sturm, and F. Fonseca. Automatic
Camera Calibration Applied to Medical Endoscopy. In 20th
British Machine Vision Conference (BMVC ’09), 2009.
B. Münzer, K. Schoeffmann, and L. Böszörmenyi. Relevance Segmentation of Laparoscopic Videos. In 2013 IEEE International Symposium on Multimedia (ISM), pages 84–91, Dec. 2013.
A. Chhatkuli, A. Bartoli, A. Malti, and T. Collins. Live image parsing in uterine laparoscopy. In IEEE International Symposium on Biomedical Imaging (ISBI), 2014.

Real-time Support at Intervention Time
Applications
§ Diagnosis support
§ Robot-assisted surgery
§ Context Awareness
§ Augmented reality
“Robotic surgical system”, Public Domain
T. Collins, D. Pizarro, A. Bartoli, M. Canis, and N. Bourdel. Computer-Assisted Laparoscopic myomectomy by augmenting the uterus with pre-operative MRI data. In 2014 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pages 243–248, Sept. 2014.
„Da Vinci Surgical System“ by Cmglee / CC BY-SA
Slightly modified from: M. P. Tjoa, S. M. Krishnan, et al. Feature extraction for the analysis of colon status from the endoscopic images. BioMedical Engineering OnLine, 2(9):1–17, 2003.

• 3D reconstruction
• Deforming tissue tracking
• Image Registration
• Instrument detection and tracking
• Surgical workflow understanding
Enabling Techniques
L. Maier-Hein, P. Mountney, A. Bartoli, H. Elhawary, D. Elson, A. Groch, A. Kolb, M. Rodrigues, J. Sorger, S. Speidel, and D. Stoyanov. Optical
techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery. Medical Image Analysis, 17(8):974–996, Dec. 2013.
S. Giannarou, M. Visentini-Scarzanella, and G. Z. Yang. Affine-invariant anisotropic detector for soft tissue tracking in minimally invasive
surgery. In Biomedical Imaging: From Nano to Macro, 2009. ISBI’09. IEEE International Symposium on, pages 1059–1062, 2009.

Post-Procedural Applications
Management and Retrieval
• Compression and storage
• Content-based retrieval
• Temporal video segmentation
• Video summarization
• Visualization & Interaction
Quality Assessment
§ Skills assessment
§ Education & Training
§ Error Rating
§ Assessment of intervention quality
M. Lux, O. Marques, K. Schöffmann, L. Böszörmenyi, and G. Lajtai. A novel tool for summarization of arthroscopic videos. Multimedia Tools and Applications, 46(2-3):521–544, Sept. 2009.
D. Liu, Y. Cao, W. Tavanapong, J. Wong, J. H. Oh, and P. C. de Groen. Quadrant coverage histogram: a new method
for measuring quality of colonoscopic procedures. In Engineering in Medicine and Biology Society, 2007. EMBS
2007. 29th Annual International Conference of the IEEE, pages 3470–3473, 2007.
J. Muthukudage, J. Oh, W. Tavanapong, J. Wong, and P. C. d. Groen. Color Based
Stool Region Detection in Colonoscopy Videos for Quality Measurements. In Y.-S. Ho,
editor, Advances in Image and Video Technology, number 7087 in Lecture Notes in
Computer Science, pages 61–72. Springer Berlin Heidelberg, Jan. 2012.

• Vision
• Archive together all relevant text, image, and video data
• Use data for information retrieval
• Support surgeons at diagnosis, surgery planning, teaching, …
• Combine different kind of data (e.g., radiology-supported surgery)
• Challenges
• Isolated systems / separation of data
• Very Big Data
• A lot of irrelevant content
• Very specific domain characteristics
• Need for domain expert knowledge
• Different communities and views
Medical Multimedia Information Systems

Post-Procedural Use of Surgery Videos

• Video recordings of endoscopic surgeries show
the same images the surgeon used for operation
• Valuable information for post-procedural use:
• Later inspection of specific moments
• Discussion of critical moments (e.g., with OP team)
• Information to patients
• Preparation of future interventions
• Forensics & investigations (e.g., comparisons)
• Training and teaching
• Surgical quality assessment (technical errors)
Video as the ’’Eye of the Surgeon’’

Full Storage of Endoscopic Videos
• Exemplary hospital
• 5 departments (Lap, Gyn, Arthro, GI, ENT)
• 2 operation rooms, each 4 ops/day, each op ca. 1-2h
• à i.e. 40 interventions per day, each ~ 90 mins.
• 60 hours video per day!
• Assumption: HD 1920x1080, H.264/AVC
• 270 GB / day (1h=4.5 GB)
• 1.9 TB / week
• 100 TB / year (200 TB MPEG-2)
4K about twice as much!
(unless encoded with H.265/HEVC)
Great challenge for a hospital’s IT department!

How to Reduce Storage Requirements?
1. Spatial compression optimization
2. Temporal compression optimization
3. Perceptual quality based optimization
Transcoding
up to 30%
up to 40%
up to 93%

Study on Video Quality
• Subjective quality assessment
• Catharina Hospital Eindhoven, NL
• 37 participants
• 19 experienced surgeons and 18 trainees
• 7 women, 30 men, average age: 40 years
• Subjective tests regarding
maximum compression
1) Perceivable quality loss
• Double-Stimulus (ITU-R BT.500-11)
• Switch between reference and test video
2) Perceivable semantic information loss
• Single Stimulus (ITU-R P.910)
• Assessing random videos (incl. reference)
Münzer, B., Schoeffmann, K., Böszörmenyi, L., Smulders, J. F., & Jakimowicz, J. J. (2014, May). Investigation of the impact of compression on the
perceptional quality of laparoscopic videos. In 2014 IEEE 27th International Symposium on Computer-Based Medical Systems (pp. 153-158). IEEE.
Session 1 Session 2

Assessment of Video Quality (Session 1)
-5
0
5
10
15
20
25
30
35
0
3000
6000
9000
12000
15000
18000
21000
24000
20 22 24 26 28 18 20 22 24 26 18 18
Difference Mean Opinion Score (DMOS)
Bitrate (Kb/s)
Test Conditions
Average bitrate Rating difference
1920x1080 1280x720 960x540 640x360
subjectively better
than reference
Reference video
(MPEG-2, HD, 20 (35) Mbit/s)
“lossless”
crf
(constant rate factor)

Assessment of Video Quality (Session 2)
1. Visually lossless with 8 Mbit/s Q1
(in comparison to 20 Mbit/s)
Reduction: 60% data vs. 0% MOS
2. Good quality with 2,5 Mbit/s and Q2
reduced resolution (1280x720)
3. Acceptable quality with 1,4 Mbit/s Q3
and lower resolution (640x360)
1
2
3

Example Videos
1280x720
Weak compression
16 MB
(crf 18)
640x360
Strong compression
0,8 MB
(crf 26)
20x

Endoscopic Video Content Analysis

1000 frames
(sampled from
17min with 1fps)
ACM Multimedia 2017 Tutorial
Medical Multimedia Information Systems (MMIS)
2
6

Content Relevance Filtering / Instrument Recognition
Münzer, B., Schoeffmann, K., & Böszörmenyi, L. (2013, December). Relevance segmentation of laparoscopic videos. In Multimedia (ISM), 2013 IEEE International Symposium on (pp. 84-91). IEEE.
Primus, M. J., Schoeffmann, K., & Böszörmenyi, L. (2015, June). Instrument classification in laparoscopic videos. In Content-Based Multimedia Indexing (CBMI), 2015 13th International Workshop on (pp. 1-6). IEEE.
Instrument detection for content understanding
(e.g., op phase segmentation, following
instruments in robot-assisted surgery)
Out-of-patient Scenes Blurry Scenes Border Area

Phase Segmentation (Cholecystectomy)
Manfred J. Primus, Klaus Schoeffmann and Laszlo Böszörmenyi. “Temporal Segmentation of Laparoscopic Videos into Surgical Phases“, in
Proceedings of the 14th International Workshop on Content-Based Multimedia Indexing (CBMI 2016), Bucharest, Romania, 2016
à Phase segmentation through instrument recognition
(color analysis, image moments, rules/heuristics)

Instrument Recognition/Tracking

Classification of OP Scene (Cataract Surgeries)
Manfred J. Primus, Doris Putzgruber-Adamitsch, Mario Taschwer, Bernd Münzer, Yosuf El-Shabrawi, Laszlo Böszörmenyi, and Klaus Schoeffmann. “Frame-Based Classification of Operation
Phases in Cataract Surgery Videos“. Proceedings of the 24th International Conference on Multimedia Modeling 2018 (MMM 2018), Bangkok, Thailand, 2018, pp. 1-12, to appear

Learning Medical Semantic (e.g., Surgical Actions)
1.105 Segments / 823.000 Frames / 9h annotated Video (out of 111 interventions)
Dissection – 58 Segs / 35.517 Pics Coagulation – 212 Segs / 84.786 Pics Cutting cold – 271 Segs / 26.388 Pics
Cutting – 106 Segs / 92.653 Pics Hysterectomy – 25 Segs / 68.466 Pics Injection – 52 Segs / 52.355 Pics
Suturing – 92 Segs / 321.851 PicsSuction & Irrigation – 173 Segs / 73.977 Pics
Petscharnig, S., & Schöffmann, K. (2017). Learning laparoscopic video shot classification for gynecological surgery. Multimedia Tools and Applications, 1-19.
WHY?
• structure video content,
• automatic indexing for retrieval,
• automatic supervision of surgeries

Deep Learning Surgical Actions
Confidence
Thresholdslow high
Petscharnig, S., & Schöffmann, K. (2017). Learning laparoscopic video shot classification for gynecological surgery. Multimedia Tools and Applications, 1-19.

Deep Learning Surgical Actions
R...Recall P...Precision

Smoke Detection
Cauterization in
90% surgeries
Instruments:
Laser or HF
(100° - 1200° C)
Current filtration system manual!
à Automatic Smoke Detection & Removal?
(Real-Time)

Automatic Smoke Detection
Achievable Performance with Saturation Peak Analysis (SPA)
Andreas Leibetseder, Manfred J. Primus, Stefan Petscharnig, and Klaus Schoeffmann. “Image-based Smoke Detection in Laparoscopic Videos“. Proceedings of Computer Assisted and Robotic Endoscopy and Clinical Image-Based
Procedures: 4th International Workshop, CARE 2017, and 6th International Workshop, CLIP 2017, held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, pp. 70-87

Automatic Smoke Detection - Performance
20K images (DS A)
10K images (DS A)
4.5K images (DS B)
SPA: Saturation Peak Analysis
GLN RGB: GoogLeNet using RGB images
GLN SAT: GoogLeNet using saturation only images
Deep Learning

Real-Time Smoke Detection Prototype

Video Interaction Tools

Desired Status
Bernd Münzer, Klaus Schoeffmann and Laszlo Boeszoermenyi. “EndoXplore: A Web-based Video Explorer for Endoscopic Videos“. Proceedings of the IEEE International Symposium on Multimedia 2017 (ISM 2017), Taipei, Taiwan, 2017, pp. 1-2

Special Content Visualization

Special Interaction Tools
Marco A. Hudelist, Sabrina Kletz, and Klaus Schoeffmann. 2016. A Multi-Video Browser for Endoscopic Videos on Tablets. In Proceedings of the 2016 ACM on Multimedia Conference (MM '16). ACM, New York, NY, USA, 722-724.
Marco A. Hudelist, Sabrina Kletz, and Klaus Schoeffmann. 2016. A Tablet Annotation Tool for Endoscopic Videos. In Proceedings of the 2016 ACM on Multimedia Conference (MM '16). ACM, New York, NY, USA, 725-727.

Surgical Quality Assessment (SQA) Software
• Integrating rating features
• More efficient video navigation/browsing
Marco A. Hudelist, Heinrich Husslein, Bernd Muenzer, Sabrina Kletz and Klaus Schoeffmann. “A Tool to Support Surgical Quality Assessment“,
in Proceedings of the Third IEEE International Conference on Multimedia Big Data (BigMM), Laguna Hills, CA, USA, 2017, pp. 238-239.

Diagnostic Decision Support
43ACM Multimedia 2017 Tutorial Medical Multimedia Information Systems (MMIS)

Challenges and Requirements

Medical knowledge transfer
Automatic
Data analysis / detection
Feedback / visualization

• Medical knowledge transfers – need DATA w/Ground Truth
• High detection accuracy
• Fast and efficient: real-time feedback and large scale
• Fit the normal examination procedures
• Adhere to ethical, legal, privacy challenges & regulations
Key Challenges & Requirements

Gastrointestinal (GI) Case Study
(challenges, system support, datasets, diagnostic decision support, ...)

• Many types of diseases can potentially affect the human gastrointestinal (GI) tract – the digestive system
• about 2.8 millions of new luminal GI cancers (esophagus, stomach, colorectal) are detected yearly
• the mortality is about 65%
• Screening of the GI tract using different types of endoscopy…
• is costly (colonoscopy according to NY Times: $1100/patient, $10 billion dollars)
• consumes valuable medical personnel time (1-2 hours)
• does not scale to large populations
• is intrusive to the patient
• …
• Current technology may potentially enable automatic algorithmic screening and assisted examinations
à a true interdisciplinary activity with high chances of societal impact
GI Tract Challenges and Potential

WHO: Colorectal Cancer Mortality 2012
Women
Men
Colorectal cancer is the third most common cause of cancer
mortality for both women and men, and it is a condition
where early detection is important for survival,
i.e., a 5-year survival probability of
going from a low 10-30% if detected in later stages
to a high 90% survival probability in early stages.
Colonoscopy it is not the ideal screening test.
Related to the cancer example, on average
20% of polyps (possible predecessors of cancer) are missed
or incompletely removed. The risk of getting cancer largely
depend on the endoscopists ability to detect and remove polyps.
A 1% increase in detection can decrease the risk of cancer with 3%.

ACM Multimedia 2017 Tutorial Medical Multimedia Information Systems (MMIS)
Live Automatic Detection
• System to assist doctors during
live endoscopy procedures
• detection accuracy depend on
experience and skills
• have a “second eye”, “better” detection
• automatic tagging, annotation of lesions
• Better procedure for documentation,
automatic report generation
50

51Medical Multimedia Information Systems (MMIS)
Video Capsule (PillCam)
§ Standard colonoscopy:
§ expensive
§ does not scale
§ intrusive
§ Wireless Video Capsule endoscopy:
§ better scale
§ less intrusive
§ possible to combine
examinations
§ watch hours of video
§ less expensive?

System Overview

Medical Knowledge Transfer
(Data Collection)

• Need more data and therefore tools to efficiently
annotate and tag data
Available GI Datasets
Name Contain Annotation Size Type Usage
CVC-ClinicDB Polyps GT masks 612 images Trad. ©, by permission
ETIS-Larib Polyp DB Polyps, Normal GT masks 1500 images Trad. ©, by permission
ASU-Mayo Clinic DB Polyps, Normal GT masks 18 videos Trad. ©, by permission
Colonoscopy Videos DB Various Lesions Sorted 76 videos Trad. Academic
Capsule Endoscopy DB Various Lesions and Findings Sorted 3170 images, 47 videos VCE Academic, by request
GastroAtlas Various Lesions and Findings Sorted, Text annotations 4449 videos Trad. Academic
WEO Atlas Various Lesions and Findings Sorted, Text annotations ? Trad. Academic
GASTROLAB Various Lesions and Findings Sorted, Text annotations ? Trad. Academic
Atlas of GE Various Lesions Sorted, Text annotations 669 images Trad. ©, by permission

• Which image is not from the same class?
… and it gets worse …
• Making a mistake between cats and dogs may not matter,
but a misclassification here may have lethal consequences
Why Can’t CS People Do the Annotation!?
PylorusZ-line Z-line Z-line Z-line Z-line

• Simple and efficient
• Web-based
• Assisted object tracking
Video Annotation Subsystem
"Expert Driven Semi-Supervised Elucidation Tool for Medical Endoscopic Videos"
Zeno Albisser, et. al.
Proceedings of tMMSys, Portland, OR, USA, March 2015

• For large collection of images
• VV / Kvasir dataset
• Fully cleaned
• Feature extraction
mechanisms
• Different unsupervised
clustering algorithms
• Hierarchical image collection
visualization
• Open source: ClusterTag
https://bitbucket.org/mpg_projects/clustertag
ClusterTag: Image Clustering and Tagging Tool
"ClusterTag: Interactive Visualization, Clustering and Tagging Tool for Big Image Collections"
Konstantin Pogorelov, et. al.
Proceedings of ICMR, Bucharest, Romania, June 2017

• Multi-Class Image Dataset for Computer Aided GI Disease Detection
• GI endoscopy images
• Some images contain the position and configuration of the endoscope (scope guide)
• 8 different anomalies and anatomical landmarks
• v1: 500 images per class, 6 pre-extracted global features
• v2: 1000 images per class
• New information added in the future: http://datasets.simula.no/kvasir/
The Kvasir Dataset
"Kvasir: A Multi-Class Image-Dataset for Computer Aided Gastrointestinal Disease Detection"
Konstantin Pogorelov, et al.
Proceedings of MMSYS, Taiwan, June 2017

• Bowel Preparation Quality Video
• 21 GI endoscopy videos of colon
• Some frames contain the position and
configuration of the endoscope (scope
guide)
• 4 classes showing four-score BBPS-
defined bowel-preparation quality
• 0 - very dirty
• …
• 3 - very clean
• http://datasets.simula.no/nerthus/
The Nerthus Dataset
"Nerthus: A Bowel Preparation Quality Video Dataset"
Konstantin Pogorelov, et al.
Proceedings of MMSYS, Taiwan, June 2017

GI Anomaly
Detection System

• Easy to extend with new diseases
• Easy to extend with new algorithms
• Easy to train
• Results are explainable?
• Disease Localization?
• Real-time?
Detection and Automatic Analysis subsystem

State-of-The-Art: Some Example Detection Systems
Polyp-Alert
• detects polyps using edges and texture
• near real-time feedback during colonoscopy (10fps)
• detected 97.7% (42 of 43) of polyp shots on 53 randomly selected
(not per frame detection)
• only 4.3% of a full-length colonoscopy procedure wrongly marked
• one of the few end-to-end systems
• Wallapak Tavanapong – from MM community

• Features extraction using open-source LIRE (Lucene Image Retrieval)
• Indexer:
• Indexing images by LIRE features for “training”
• Classifier:
• Built-in benchmarking functionality
• Output to console & JSON / HTML
• Verified with different datasets and use cases, e.g.,
life-logging, recommender systems, network analysis, etc.
• Open source project – OpenSea
Global Features (GF)-Based Detection
”EIR - Efficient Computer Aided Diagnosis Framework for Gastrointestinal Endoscopies"
Michael Riegler, et. al.
Proceedings of CBMI, Bucharest, Romania, June 2016

• Search for an optimal combination of
global image feature descriptors
• Combining results by late fusion
• LIRE image feature descriptors
JCD and Tamura are the best choice
Original polyp Color feature Edge and color Texture Edge

Feature
extractors
Features
Features
Polyps
Cancer
Feature
extractors
Features
Normal
Distance to
the training
images
Class
selection for
each feature
Distance
Distance
Polyps
Cancer
Distance
Normal
Index of the
training set
Late fusion
Image
class

• With many enough CPUs,
the detection runs in
real-time
• GPU-acceleration
66
Java CUDAC++
""GPU-accelerated Real-time Gastrointestinal Diseases Detection"
Proceedings of CBMS, Dublin, Ireland/Belfast, Northern Ireland, June 2016

• Tensorflow as backend
• Based on Inception v3
• Last layers removed
• Model retrained on medical data
• Applying simple transformations to increase
size of training set
• Very long training time
• Applying model is fast
Basic CNN-Based Detection
“Efficient disease detection in gastrointestinal videos - global features versus neural networks"
Multimedia Tools and Applications, 2017

Performance
(accuracy and speed)

§ Mayo dataset (18781 images/frames)
§ masks for all polyps
• GF:
• recall 98.50%, precision 93.88%, fps ~300
• CNN:
• Modified Inception v3: recall 95.86%, precision 80.78%, fps: ~30
• Inception v3 + WEKA: recall: 88.87%, precision: 89.16%, fps: ~30
ASU Mayo Dataset: Polyp Detection
”EIR - Efficient Computer Aided Diagnosis Framework for Gastrointestinal Endoscopies"
Proceedings of CBMI, Bucharest, Romania, June 2016

• Resource consumption and processing performance of GF:
• Neural networks (also including GPU support)?
• tests so far: ~30 fps (same GPU as above)
• but adding layers, more networks, … !?? (newer GPU)
• Inception v3 TFL: 66 fps, plain CNN: ~40-45 fps
• GAN: ~12 fps (for 160x160)
ASU Mayo Dataset: Polyp Detection

• Process only frames containing polyps
• Performs image enhancement
• Detects curve-shaped objects and
local maximums
• Builds energy map and selects
4 possible locations
• Localization performance:
• recall 31.83 %,
• precision 32.07%
• ~30 fps
• later better GPU: ~75 fps (detection: 300 fps ; localization 100 fps)
ASU Mayo Dataset: First Try for Polyp Localization

• Vestre Viken (VV) multi-disease dataset (250 images per class)
• GF:
• recall 90.60 %
• precision 91.40%
• fps ~30
• CNN:
• recall: 87.20%
• precision: 87.90%
• fps: ~30
VV Dataset: Multi-Disease Detection
""Efficient disease detection in gastrointestinal videos - global features versus neural networks"

• GF
• CNN
VV Dataset: Multi-Disease Detection
""Efficient disease detection in gastrointestinal videos - global features versus neural networks"

• 7 different algorithms
• Convolutional neural networks (CNN) (2) – trained from scratch
• 3-layers
• 6-layers
• Transfer learning (1) – retrained Inception v3
• Global features (4)
• 2 global features (JCD, Tamura)
• 6 global features (JCD, Tamura, Color Layout, Edge Histogram, Auto Color Correlogram and PHOG)
• 2 different algorithms (Random forest and logistic model tree)
• 2 baselines
• Random Forrest with one global feature
• Majority class
• 2-folded cross validation
Kvasir Dataset v1: Multi-Disease Detection

Dyed and Lifted PolypDyed Resection Margin

CecumPylorus

• Using same GF and some new deep features, i.e.,
• Pre-trained ImageNet dataset Inception v3
• ResNet50 models
• Used different ML classifications;
• random tree (RT)
• random forest (RF)
• logistic model tree (LMR) – performed best
• Uses weights of 1000 pre-defined concepts as
features
• Top layer input as features vector
(16384 for Inception v3 and 2048 for ResNet50)
Kvasir Dataset v1 à v2: Multi-Disease Detection
Pretrained
model
Output or top-
layer input
weights
WEKA for
classification
78
Team Approaches F1 FPS
SCL-UMD Global-features and deep-features extraction,
Inception-V3 and VGGNet CNN models, followed by
machine-learning-based classification using RT, RF, SVM
and LMR classifiers
0.848 1.3
FAST-NU-DS Global and local features combined followed by data size
reduction by applying K-means clustering and than
using logistic regression model for the classification
0.767 2.3
ITEC-AAU Two different custom Inception-like CNN models 0.755 1.4
HKBU A manifold learning method (bidirectional marginal
Fisher analysis) learning a compact representation of the
data, then machine-learning-based multi-class support
vector machine is used for the classification
0.703 2.2
SIMULA GF-features extraction, ResNet50 and Inception-V3 CNN
models and followed by machine-learning-based
classification using RT, RF and LMR classifiers
0.826 46.0

• 7 different algorithms
• Convolutional neural networks (CNN) (2) – trained from scratch
• 3-layers
• 6-layers
• Transfer learning (1) – retrained Inception v3
• Global features (4)
• 2 global features (JCD, Tamura)
• 6 global features
(JCD, Tamura, Color Layout, Edge Histogram, Auto Color Correlogram and PHOG)
• 2 different algorithms (Random forest and logistic model tree)
• 2 baselines
• Random Forrest with one global feature
• Majority class
• 2-folded cross validation
Nerthus Dataset: Bowel Cleanness Level

• Too little data
• Blurry images due to camera motion
• Objects too close to camera
• Under or over scene lighting
• Flares
• Artificial objects and natural “contaminations”
• Low resolution of capsular endoscopes
• …
Data Challenges: Preprocessing

Data Enhancements for CNN Training

Detection Feedback

Detection Subsystem Outputs
• Visualize the output of the system to the medical doctors
• Simple and easy to understand
• Live support
• Useable for automatic reports, etc.

• Polyps
• Input:
Camera or Video files
• Output:
Live stream and
Performance reports
• Full HD
• Real-time: 30 FPS
Real-time Detection Feedback

So, all problems solved!!??

• Improve detection, localization and system performance
(retrieval, machine learning, features, search, real-time, distributed computing, scale, visualization, neural networks, user
interaction, object tracking, …)
1. Exploiting domain expert knowledge – build datasets
2. Integration of various data, multi-modality – new sensors
3. Explainable AI
4. Automated report system
5. Full system integration
6. Patient context information
7. Visualization, decision support
8. Integration of data from various sources / systems
9. Other areas in medicine
10. …
Many more…
Many Open Challenges…
"Multimedia and Medicine: Teammates for Better Disease Detection and Survival"
Proceedings ACM MM, Amsterdam, The Netherlands, October 2016

• We have given several case-specific examples, but in general, they are common for MMIS
• Doctors want to use all the data for general support:
analysis, diagnostics, reporting, teaching, statistics, similarity search / comparisons, …
• Currently, …
• more and more high quality data is recorded / produced
• data analysis methods are (only) promising
• good visualization tools exist, but not used (e.g., AR, VR, …)
• some tools are missing
• many (other) areas produce separate (isolated) methods
• …
• but, we need a complete integrated system!
Ø Our multimedia community is needed
Summary

The End…

Medical Multimedia Information Systems (ACMMM17 Tutorial)

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