Questions and Answers ​in MRI
  • Home
  • Complete List of Questions
  • …Magnets & Scanners
    • Basic Electromagnetism >
      • What causes magnetism?
      • What is a Tesla?
      • Who was Tesla?
      • What is a Gauss?
      • How strong is 3.0T?
      • What is a gradient?
      • Aren't gradients coils?
      • What is susceptibility?
      • How to levitate a frog?
      • What is ferromagnetism?
      • Superparamagnetism?
    • Magnets - Part I >
      • Types of magnets?
      • Brands of scanners?
      • Which way does field point?
      • Which is the north pole?
      • Low v mid v high field?
      • Advantages to low-field?
      • Disadvantages?
      • What is homogeneity?
      • Why homogeneity?
      • Why shimming?
      • Passive shimming?
      • Active shimming?
    • Magnets - Part II >
      • Superconductivity?
      • Perpetual motion?
      • How to ramp?
      • Superconductive design?
      • Room Temp supercon?
      • Liquid helium use?
      • What is a quench?
      • Is field ever turned off?
      • Emergency stop button?
    • Gradients >
      • Gradient coils?
      • How do z-gradients work?
      • X- and Y- gradients?
      • Open scanner gradients?
      • Eddy current problems?
      • Active shielded gradients?
      • Active shield confusion?
      • What is pre-emphasis?
      • Gradient heating?
      • Gradient specifications?
      • Gradient linearity?
    • RF & Coils >
      • Many kinds of coils?
      • Radiofrequency waves?
      • Phase v frequency?
      • RF Coil function(s)?
      • RF-transmit coils?
      • LP vs CP (Quadrature)?
      • Multi-transmit RF?
      • Receive-only coils?
      • Array coils?
      • AIR Coils?
    • Site Planning >
      • MR system layout?
      • What are fringe fields?
      • How to reduce fringe?
      • Magnetic shielding?
      • Need for vibration testing?
      • What's that noise?
      • Why RF Shielding?
      • Wires/tubes thru wall?
  • ...Safety and Screening
    • Overview >
      • ACR Safety Zones?
      • MR safety screening?
      • Incomplete screening?
      • Passive v active implants?
      • Conditional implants?
      • Common safety issues?
      • Projectiles?
      • Metal detectors?
      • Pregnant patients?
      • Postop, ER & ICU patients?
      • Temperature monitoring?
      • Orbital foreign bodies?
      • Bullets and shrapnel?
    • Static Fields >
      • "Dangerous" metals?
      • "Safe" metals?
      • Magnetizing metal?
      • Object shape?
      • Forces on metal?
      • Most dangerous place?
      • Force/torque testing?
      • Static field bioeffects?
      • Dizziness/Vertigo?
      • Flickering lights?
      • Metallic taste?
    • RF Fields >
      • RF safety overview?
      • RF biological effects?
      • What is SAR?
      • SAR limits?
      • Operating modes?
      • How to reduce SAR?
      • RF burns?
      • Estimate implant heating?
      • SED vs SAR?
      • B1+rms vs SAR?
      • Personnel exposure?
      • Cell phones?
    • Gradient Fields >
      • Gradient safety overview
      • Acoustic noise?
      • Nerve stimulation?
      • Gradient vs RF heating?
    • Safety: Neurological >
      • Aneurysm coils/clips?
      • Shunts/drains?
      • Pressure monitors/bolts?
      • Deep brain stimulators?
      • Spinal cord stimulators?
      • Vagal nerve stimulators?
      • Cranial electrodes?
      • Carotid clamps?
      • Peripheral stimulators?
      • Epidural catheters?
    • Safety: Head & Neck >
      • Additional orbit safety?
      • Cochlear Implants?
      • Bone conduction implants?
      • Other ear implants?
      • Dental/facial implants?
      • ET tubes & airways?
    • Safety: Chest & Vascular >
      • Breast tissue expanders?
      • Breast biopsy markers?
      • Airway stents/valves/coils?
      • Respiratory stimulators?
      • Ports/vascular access?
      • Swan-Ganz catheters?
      • IVC filters?
      • Implanted infusion pumps?
      • Insulin pumps & CGMs?
      • Vascular stents/grafts?
      • Sternal wires/implants?
    • Safety: Cardiac >
      • Pacemaker dangers?
      • Pacemaker terminology?
      • New/'Safe" Pacemakers?
      • Old/Legacy Pacemakers?
      • Violating the conditions?
      • Epicardial pacers/leads?
      • Cardiac monitors?
      • Heart valves?
      • Miscellaneous CV devices?
    • Safety: Abdominal >
      • PIllCam and capsules?
      • Gastric pacemakers?
      • Other GI devices?
      • Contraceptive devices?
      • Foley catheters?
      • Incontinence devices?
      • Penile Implants?
      • Sacral nerve stimulators?
      • GU stents and other?
    • Safety: Orthopedic >
      • Orthopedic hardware?
      • External fixators?
      • Traction and halos?
      • Bone stimulators?
      • Magnetic rods?
  • …The NMR Phenomenon
    • Spin >
      • What is spin?
      • Why I = ½, 1, etc?
      • Proton = nucleus = spin?
      • Predict nuclear spin (I)?
      • Magnetic dipole moment?
      • Gyromagnetic ratio (γ)?
      • "Spin" vs "Spin state"?
      • Energy splitting?
      • Fall to lowest state?
      • Quantum "reality"?
    • Precession >
      • Why precession?
      • Who was Larmor?
      • Energy for precession?
      • Chemical shift?
      • Net magnetization (M)?
      • Does M instantly appear?
      • Does M also precess?
      • Does precession = NMR?
    • Resonance >
      • MR vs MRI vs NMR?
      • Who discovered NMR?
      • How does B1 tip M?
      • Why at Larmor frequency?
      • What is flip angle?
      • Spins precess after 180°?
      • Phase coherence?
      • Release of RF energy?
      • Rotating frame?
      • Off-resonance?
      • Adiabatic excitation?
      • Adiabatic pulses?
    • Relaxation - Physics >
      • Bloch equations?
      • What is T1?
      • What is T2?
      • Relaxation rate vs time?
      • Why is T1 > T2?
      • T2 vs T2*?
      • Causes of Relaxation?
      • Dipole-dipole interactions?
      • Chemical Exchange?
      • Spin-Spin interactions?
      • Macromolecule effects?
      • Which H's produce signal?
      • "Invisible" protons?
      • Magnetization Transfer?
      • Bo effect on T1 & T2?
      • How to predict T1 & T2?
    • Relaxation - Clincial >
      • T1 bright? - fat
      • T1 bright? - other oils
      • T1 bright? - cholesterol
      • T1 bright? - calcifications
      • T1 bright? - meconium
      • T1 bright? - melanin
      • T1 bright? - protein/mucin
      • T1 bright? - myelin
      • Magic angle?
      • MT Imaging/Contrast?
  • …Pulse Sequences
    • MR Signals >
      • Origin of MR signal?
      • Free Induction Decay?
      • Gradient echo?
      • TR and TE?
      • Spin echo?
      • 90°-90° Hahn Echo?
      • Stimulated echoes?
      • STEs for imaging?
      • 4 or more RF-pulses?
      • Partial flip angles?
      • How is signal higher?
      • Optimal flip angle?
    • Spin Echo >
      • SE vs Multi-SE vs FSE?
      • Image contrast: TR/TE?
      • Opposite effects ↑T1 ↑T2?
      • Meaning of weighting?
      • Does SE correct for T2?
      • Effect of 180° on Mz?
      • Direction of 180° pulse?
    • Inversion Recovery >
      • What is IR?
      • Why use IR?
      • Phase-sensitive IR?
      • Why not PSIR always?
      • Choice of IR parameters?
      • TI to null a tissue?
      • STIR?
      • T1-FLAIR
      • T2-FLAIR?
      • IR-prepped sequences?
      • Double IR?
    • Gradient Echo >
      • GRE vs SE?
      • Multi-echo GRE?
      • Types of GRE sequences?
      • Commercial Acronyms?
      • Spoiling - what and how?
      • Spoiled-GRE parameters?
      • Spoiled for T1W only?
      • What is SSFP?
      • GRASS/FISP: how?
      • GRASS/FISP: parameters?
      • GRASS vs MPGR?
      • PSIF vs FISP?
      • True FISP/FIESTA?
      • FIESTA v FIESTA-C?
      • DESS?
      • MERGE/MEDIC?
      • GRASE?
      • MP-RAGE v MR2RAGE?
    • Susceptibility Imaging >
      • What is susceptibility (χ)?
      • What's wrong with GRE?
      • Making an SW image?
      • Phase of blood v Ca++?
      • Quantitative susceptibility?
    • Diffusion: Basic >
      • What is diffusion?
      • Iso-/Anisotropic diffusion?
      • "Apparent" diffusion?
      • Making a DW image?
      • What is the b-value?
      • b0 vs b50?
      • Trace vs ADC map?
      • Light/dark reversal?
      • T2 "shine through"?
      • Exponential ADC?
      • T2 "black-out"?
      • DWI bright causes?
    • Diffusion: Advanced >
      • Diffusion Tensor?
      • DTI (tensor imaging)?
      • Whole body DWI?
      • Readout-segmented DWI?
      • Small FOV DWI?
      • IVIM?
      • Diffusion Kurtosis?
    • Fat-Water Imaging >
      • Fat & Water properties?
      • F-W chemical shift?
      • In-phase/out-of-phase?
      • Best method?
      • Dixon method?
      • "Fat-sat" pulses?
      • Water excitation?
      • STIR?
      • SPIR?
      • SPAIR v SPIR?
      • SPIR/SPAIR v STIR?
  • …Making an Image
    • From Signals to Images >
      • Phase v frequency?
      • Angular frequency (ω)?
      • Signal squiggles?
      • Real v Imaginary?
      • Fourier Transform (FT)?
      • What are 2D- & 3D-FTs?
      • Who invented MRI?
      • How to locate signals?
    • Frequency Encoding >
      • Frequency encoding?
      • Receiver bandwidth?
      • Narrow bandwidth?
      • Slice-selective excitation?
      • SS gradient lobes?
      • Cross-talk?
      • Frequency encode all?
      • Mixing of slices?
      • Two slices at once?
      • Simultaneous Multi-Slice?
    • Phase Encoding >
      • Phase-encoding gradient?
      • Single PE step?
      • What is phase-encoding?
      • PE and FE together?
      • 2DFT reconstruction?
      • Choosing PE/FE direction?
    • Performing an MR Scan >
      • What are the steps?
      • Automatic prescan?
      • Routine shimming?
      • Coil tuning/matching?
      • Center frequency?
      • Transmitter gain?
      • Receiver gain?
      • Dummy cycles?
      • Where's my data?
      • MR Tech qualifications?
    • Image Quality Control >
      • Who regulates MRI?
      • Who accredits?
      • Mandatory accreditation?
      • Routine quality control?
      • MR phantoms?
      • Geometric accuracy?
      • Image uniformity?
      • Slice parameters?
      • Image resolution?
      • Signal-to-noise?
      • Ghosting?
  • …K-space & Rapid Imaging
    • K-space (Basic) >
      • What is k-space?
      • Parts of k-space?
      • What does "k" stand for?
      • Spatial frequencies?
      • Locations in k-space?
      • Data for k-space?
      • Why signal ↔ k-space?
      • Spin-warp imaging?
      • Big spot in middle?
      • K-space trajectories?
      • Radial sampling?
    • K-space (Advanced) >
      • K-space grid?
      • Negative frequencies?
      • Field-of-view (FOV)
      • Rectangular FOV?
      • Partial Fourier?
      • Phase symmetry?
      • Read symmetry?
      • Why not use both?
      • ZIP?
    • Rapid Imaging (FSE &EPI) >
      • What is FSE/TSE?
      • FSE parameters?
      • Bright Fat?
      • Other FSE differences?
      • Dual-echo FSE?
      • Driven equilibrium?
      • Reduced flip angle FSE?
      • Hyperechoes?
      • SPACE/CUBE/VISTA?
      • Echo-planar imaging?
      • HASTE/SS-FSE?
    • Parallel Imaging (PI) >
      • What is PI?
      • How is PI different?
      • PI coils and sequences?
      • Why and when to use?
      • Two types of PI?
      • SENSE/ASSET?
      • GRAPPA/ARC?
      • CAIPIRINHA?
      • Compressed sensing?
      • Noise in PI?
      • Artifacts in PI?
  • …Contrast Agents
    • Contrast Agents: Physics >
      • Why Gadolinium?
      • Paramagnetic relaxation?
      • What is relaxivity?
      • Why does Gd shorten T1?
      • Does Gd affect T2?
      • Gd & field strength?
      • Best T1-pulse sequence?
      • Triple dose and MT?
      • Dynamic CE imaging?
      • Gadolinium on CT?
    • Contrast Agents: Clinical >
      • So many Gd agents!
      • Important properties?
      • Ionic v non-ionic?
      • Intra-articular/thecal Gd?
      • Gd liver agents (Eovist)?
      • Mn agents (Teslascan)?
      • Feridex & Liver Agents?
      • Lymph node agents?
      • Ferumoxytol?
      • Blood pool (Ablavar)?
      • Bowel contrast agents?
    • Contrast Agents: Safety >
      • Gadolinium safety?
      • Allergic reactions?
      • Renal toxicity?
      • What is NSF?
      • NSF by agent?
      • Informed consent for Gd?
      • Gd protocol?
      • Is Gd safe in infants?
      • Reduced dose in infants?
      • Gd in breast milk?
      • Gd in pregnancy?
      • Gd accumulation?
      • Gd deposition disease?
  • …Cardiovascular and MRA
    • Flow effects in MRI >
      • Defining flow?
      • Expected velocities?
      • Laminar v turbulent?
      • Predicting MR of flow?
      • Time-of-flight effects?
      • Spin phase effects?
      • Flow void?
      • Why GRE ↑ flow signal?
      • Slow flow v thrombus?
      • Even-echo rephasing?
      • Flow-compensation?
      • Flow misregistration?
    • MR Angiography - I >
      • MRA methods?
      • Dark vs bright blood?
      • Time-of-Flight (TOF) MRA?
      • 2D vs 3D MRA?
      • MRA parameters?
      • Magnetization Transfer?
      • Ramped flip angle?
      • MOTSA?
      • Fat-suppressed MRA?
      • TOF MRA Artifacts?
      • Phase-contrast MRA?
      • What is VENC?
      • Measuring flow?
      • 4D Flow Imaging?
      • How accurate?
    • MR Angiography - II >
      • Gated 3D FSE MRA?
      • 3D FSE MRA parameters?
      • SSFP MRA?
      • Inflow-enhanced SSFP?
      • MRA with ASL?
      • Other MRA methods?
      • Contrast-enhanced MRA?
      • Timing the bolus?
      • View ordering in MRA?
      • Bolus chasing?
      • TRICKS or TWIST?
      • CE-MRA artifacts?
    • Cardiac I - Intro/Anatomy >
      • Cardiac protocols?
      • Patient prep?
      • EKG problems?
      • Magnet changes EKG?
      • Gating v triggering?
      • Gating parameters?
      • Heart navigators?
      • Dark blood/Double IR?
      • Why not single IR?
      • Triple IR?
      • Polar plots?
      • Coronary artery MRA?
    • Cardiac II - Function >
      • Beating heart movies?
      • Cine parameters?
      • Real-time cine?
      • Ventricular function?
      • Tagging/SPAMM?
      • Perfusion: why and how?
      • 1st pass perfusion?
      • Quantifying perfusion?
      • Dark rim artifact
    • Cardiac III - Viability >
      • Gd enhancement?
      • TI to null myocardium?
      • PS (phase-sensitive) IR?
      • Wideband LGE?
      • T1 mapping?
      • Iron/T2*-mapping?
      • Edema/T2-mapping?
      • Why/how stress test?
      • Stess drugs/agents?
      • Stress consent form?
  • …MR Artifacts
    • Tissue-related artifacts >
      • Chemical shift artifact?
      • Chemical shift in phase?
      • Reducing chemical shift?
      • Chemical Shift 2nd Kind?
      • In-phase/out-of phase?
      • IR bounce point?
      • Susceptibility artifact?
      • Metal suppression?
      • Dielectric effect?
      • Dielectric Pads?
    • Motion-related artifacts >
      • Why discrete ghosts?
      • Motion artifact direction?
      • Reducing motion artifacts?
      • Saturation pulses?
      • Gating methods?
      • Respiratory comp?
      • Navigator echoes?
      • PROPELLER/BLADE?
    • Technique-related artifacts >
      • Partial volume effects?
      • Slice overlap?
      • Aliasing?
      • Wrap-around artifact?
      • Eliminate wrap-around?
      • Phase oversampling?
      • Frequency wrap-around?
      • Spiral/radial artifacts?
      • Gibbs artifact?
      • Nyquist (N/2) ghosts?
      • Zipper artifact?
      • Data artifacts?
      • Surface coil flare?
      • MRA Artifacts (TOF)?
      • MRA artifacts (CE)?
  • …Functional Imaging
    • Perfusion I: Intro & DSC >
      • Measuring perfusion?
      • Meaning of CBF, MTT etc?
      • DSC v DCE v ASL?
      • How to perform DSC?
      • Bolus Gd effect?
      • T1 effects on DSC?
      • DSC recirculation?
      • DSC curve analysis?
      • DSC signal v [Gd]
      • Arterial input (AIF)?
      • Quantitative DSC?
    • Perfusion II: DCE >
      • What is DCE?
      • How is DCE performed?
      • How is DCE analyzed?
      • Breast DCE?
      • DCE signal v [Gd]
      • DCE tissue parmeters?
      • Parameters to images?
      • K-trans = permeability?
      • Utility of DCE?
    • Perfusion III: ASL >
      • What is ASL?
      • ASL methods overview?
      • CASL?
      • PASL?
      • pCASL?
      • ASL parameters?
      • ASL artifacts?
      • Gadolinium and ASL?
      • Vascular color maps?
      • Quantifying flow?
    • Functional MRI/BOLD - I >
      • Who invented fMRI?
      • How does fMRI work?
      • BOLD contrast?
      • Why does BOLD ↑ signal?
      • Does BOLD=brain activity?
      • BOLD pulse sequences?
      • fMRI Paradigm design?
      • Why "on-off" comparison?
      • Motor paradigms?
      • Visual?
      • Language?
    • Functional MRI/BOLD - II >
      • Process/analyze fMRI?
      • Best fMRI software?
      • Data pre-processing?
      • Registration/normalization?
      • fMRI statistical analysis?
      • General Linear Model?
      • Activation "blobs"?
      • False activation?
      • Resting state fMRI?
      • Analyze RS-fMRI?
      • Network/Graphs?
      • fMRI at 7T?
      • Mind reading/Lie detector?
      • fMRI critique?
  • …MR Spectroscopy
    • MRS I - Basics >
      • MRI vs MRS?
      • Spectra vs images?
      • Chemical shift (δ)?
      • Measuring δ?
      • Backward δ scale?
      • Predicting δ?
      • Size/shapes of peaks?
      • Splitting of peaks?
      • Localization methods?
      • Single v multi-voxel?
      • PRESS?
      • STEAM?
      • ISIS?
      • CSI?
    • MRS II - Clinical ¹H MRS >
      • How-to: brain MRS?
      • Water suppression?
      • Fat suppression?
      • Normal brain spectra?
      • Choice of TR/TE/etc?
      • Hunter's angle?
      • Lactate inversion?
      • Metabolite mapping?
      • Metabolite quantitation?
      • Breast MRS?
      • Gd effect on MRS?
      • How-to: prostate MRS?
      • Prostate spectra?
      • Muscle ¹H-MRS?
      • Liver ¹H-MRS?
      • MRS artifacts?
    • MRS III - Multi-nuclear >
      • Other nuclei?
      • Why phosphorus?
      • How-to: ³¹P MRS
      • Normal ³¹P spectra?
      • Organ differences?
      • ³¹P measurements?
      • Decoupling?
      • NOE?
      • Carbon MRS?
      • Sodium imaging?
      • Xenon imaging?
  • ...Artificial Intelligence
    • AI Part I: Basics >
      • Artificial Intelligence (AI)?
      • What is a neural network?
      • Machine Learning (ML)?
      • Shallow v Deep ML?
      • Shallow networks?
      • Deep network types?
      • Data prep and fitting?
      • Back-Propagation?
      • DL 'Playground'?
    • AI Part 2: Advanced >
      • What is convolution?
      • Convolutional Network?
      • Softmax?
      • Upsampling?
      • Limitations/Problems of AI?
      • Is the Singularity near?
    • AI Part 3: Image processing >
      • AI in clinical MRI?
      • Super-resolution?
  • ...Tissue Properties Imaging
    • MRI of Hemorrhage >
      • Hematoma overview?
      • Types of Hemoglobin?
      • Hyperacute/Oxy-Hb?
      • Acute/Deoxy-Hb?
      • Subacute/Met-Hb?
      • Deoxy-Hb v Met-Hb?
      • Extracellular met-Hb?
      • Chronic hematomas?
      • Hemichromes?
      • Ferritin/Hemosiderin?
      • Subarachnoid blood?
      • Blood at lower fields?
    • T2 cartilage mapping
    • MR Elastography?
    • Synthetic MRI?
    • Amide Proton Transfer?
    • MR thermography?
    • Electric Properties Imaging?
  • Copyright/Legal
    • Copyright Issues
    • Legal Disclaimers
  • Forums/Blogs/Links
  • What's New
  • Self-test Quizzes - NEW!
    • Magnets & Scanners Quiz
    • Safety & Screening Quiz
    • NMR Phenomenon Quiz
    • Pulse Sequences Quiz
    • Making an Image Quiz
    • K-space & Rapid Quiz
    • Contrast & Blood Quiz
    • Cardiovascular & MRA Quiz

Convolutional Network

What is a convolutional neural network (CNN) and how does it work?
convolutional nerual network CNN
In the last several Q&A's, we have finally developed a background to understand how a convolutional neural network (CNN) is constructed.  In this Q&A we will try to put the pieces all together, expanding on the diagram below representative of a series-type CNN (like VGGNet) whose input might be an MR image and output a disease prediction/classification.
Picture
A simple CNN for disease classification
​Convolution
Convolution of the input image is the initial step in a CNN. As described in the prior Q&A, convolution involves a sliding multiplication and addition of regional data points by a small (typically 3x3 or 5x5) array of numbers called a kernel or filter. By using sets of different kernels (such as ones that emphasize edges or shapes) a set of regional feature maps is produced. 
Note that during training of the network, the filters at each layer will change gradually until they are optimized for the particular purpose intended (e.g., segmentation, classification).
First level feature map brain
First-level feature maps of a brain MRI extracted from a convolutional network. Each small image is the result of convolution with one of 64 different first-level filters that emphasize various simple properties, including bright vs dark regions, edges, curves, and shadows.
Activation
An activation function is also commonly applied during feature map generation to constrain output pixels to a certain range. As described in a prior Q&A, a common choice is the Rectified Linear Unit (ReLU) function that passes through only positive values to the feature map and sets all negative pixels to zero.
ReLU
ReLU
Appearance of one of the above 64 feature maps before and after application of ReLU activation function
Pooling (Downsampling)
​
The next step, pooling, shrinks the size of each feature map by 75% or more. This allows only the most important features to be retained, thus reducing the number of learnable features of the model. With fewer parameters, pooling helps prevent overfitting of data. Pooling also reduces spatial invariance, meaning the network is able to recognize features even though they may be distorted or rotated.
max and avr pooling
Max vs Average Pooling with a 2x2 matrix
The most popular method is known as Max Pooling and uses a 2x2 filter to operate on each feature map.  As shown in the illustration, the Max Pooling filter takes the largest value from each group of 4 pixels, thus shrinking the image substantially. A second common method is Average (Avg) Pooling, where the average value from each group of 4 pixels is taken. Other techniques include Min Pooling, Global Pooling, Stochastic Pooling, and 1-2 Norm Pooling. ​
Max and average pooling
Max and average pooling of feature map using a 2x2 matrix
Convolution, Activation, and Pooling ..... Repeat
The convolution-pooling process may be repeated several times, producing ever smaller "feature of feature" maps. Pooling is not always performed after each convolution; in some networks several convolutions are performed followed by a pooling step.
A common question that arises is what to these inner networks doing and what do deeper level feature maps look like?
Although the results depend upon the particular network under consideration and filters selected, the following statements generally apply:
  1. The initial feature maps reflect the presence of lines, edges, and simple regions of light and dark within localized areas of the original image spanned by the convolution kernel.
  2. Subsequent early layers are sensitive to curved contours and the junction of lines, recognizing loops and corners. Ultimately whole shapes like rectangles and circles may arise.
  3. Deeper convolutions produce maps sensitive to texture and surfaces.
  4. With progressive convolutions and pooling, the feature maps become progressively smaller and coarser.  They are no longer localized but contain information from all parts of the original image.
  5. These nested "features of features" maps may become largely unrecognizable to our eyes, reflecting hidden patterns and relationships between objects detected at earlier stages.
  6. With training the filter weightings are modified to become optimized for the specific task required.
The image and Deep Visualization Toolbox video by Jason Yosinki below may help you get a better feeling about what the computer is "seeing" in these deeper layers.
Convolutional layers visualizaiton
Original image and feature maps after 1 to 15 convolutions. (Adapted from Yosinski under CC-BY)
Final Processing Steps
Many possibilities exist once the multiple convolution-pooling process is complete. One common final stage, known as vectorization or flattening, converts the last 2-dimensional feature maps into a single long chain of numbers (i.e., a "vector"). The vectorized feature maps then serve as inputs into a fully connected neural network whose output can be used to produce a final "decision" (such as tissue segmentation or disease classification).  The fully connected layers allow the system to combine various combinations of information from the vectorized feature maps in making the system's end result. To have statistical meaning, the raw data in the output vector must be transformed into probabilities by use of the Softmax function (described in more detail in the next Q&A).  Alternatively, the fully pooled data may be re-expanded back to its original dimensions, a process known as up-pooling, outlined in the a subsequent Q&A.
CNN Classification; Softmax
Typical final processing steps in a CNN used for image classification/segmentation. Vectorized feature maps are passed through several fully connected layers to produce a numerical output vector (Z). The Softmax function converts these raw numbers into probabilities.

Advanced Discussion (show/hide)»

No supplementary material yet. Check back soon!

References
     Ajit A, Acharya K, Semanta A. A review of convolutional neural networks. 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE):1-5. [DOI LINK]
     Amidi A, Amidi S. VIP Cheatsheet for CS 230 - Deep Learning: Convolutional Neural Networks. Stanford University; 2019:1-5.  [Downloaded from this Link 6-22-22]
     Brownlee J. How to visualize filters and feature maps in convolutional neural networks. In: Deep Learning for Computer Vision. 2019:1-17.  
     Yosinski J, Clune J, Nguyen A, et al. Understanding neural networks through deep visualization. ArXiv 2015;1506.06579.  [ArXiv Link]
     Zeiler MD, Fergus R. Visualizing and understanding convolutional networks. ECCV 2014, Arxiv 13.11.2901 (Nov 28,2013) [ArXiv LINK]

Related Questions
     I still don't understand how machines learn. How do they "reprogram" themselves?
     What does the term convolution mean? 
     What is the Softmax function and why is it needed?​

←  Previous Question
Next Question  →
↑ Complete List of Questions ↑
© 2024 AD Elster, ELSTER LLC
All rights reserved.   
MRIquestions.com - Home
Donate
Please help keep this site free for everyone in the world!