Guide-yakabatana mibvunzo · Pakati Nhanho

AI muMedical Imaging Quiz

Understand how imaging models support detection, segmentation and triage, and why local validation matters before clinical use.

Nzira dzekutungamira dzinoenderanaAI Mune Medical Imaging
Mubvunzo 1 ye 8

Ndeipi mhando yeneural network yanga iri hwaro hwekuongorora mifananidzo yekurapa?

Tarisa mubvunzo wese8 mibvunzo

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

  1. Ndeipi mhando yeneural network yanga iri hwaro hwekuongorora mifananidzo yekurapa?
    • Convolutional neural network (CNNs)
    • Recurrent network yeaudio
    • Sarudzo miti
    • Spreadsheet macros
  2. Chii chinoitwa neViz.ai system?
    • Inoronga mamenu ekudyira kuchipatara
    • Inoongorora ma CT scans kuti amire anofungidzirwa kuti mavhiri emidziyo mihombe uye kunyevera timu
    • Inoshandura mishumo yeradiology kuita mimhanzi
    • Inotsiva iyo CT scanner Hardware
  3. Chii chinonzi U-Net chivakwa chinowanzo shandiswa mukufungidzira?
    • Kugadzira mazwi enziyo
    • Kumanikidza mitambo yemavhidhiyo
    • Per-pixel segmentation, sekutsanangura bundu kana nhengo
    • Kuvharidzira mapassword evarwere
  4. What regulatory pathway did FDA use for the named Viz.ai imaging device?
    • A blanket approval covering all imaging-AI tools
    • A drug marketing authorization
    • An exemption from all clinical evidence requirements
    • A De Novo classification decision for a specific device and intended use
  5. Chikonzero chakakosha chekufungidzira kweAI chingatadza pane data rechipatara chitsva ndechekuti:
    • Misiyano yemhando ye scanner, protocol, kana huwandu hwevarwere kubva pakudzidziswa data
    • Ma scan ane mavara akanyanya
    • Iyo modhi haifarire chivakwa
    • Mifananidzo inogara yakafanana kwese kwese
  6. What do U-Net skip connections carry from the encoder to the decoder?
    • A patient’s medical record number
    • Higher-resolution feature maps that help recover spatial detail
    • A radiology report’s billing code
    • Only a global class label
  7. Why should medical-imaging datasets generally be split at the patient level?
    • To make every scanner appear in only one dataset
    • To guarantee equal disease prevalence
    • To reduce leakage from multiple correlated images of the same patient across train and test sets
    • To remove the need for external validation
  8. What does probability calibration assess in an imaging model?
    • Whether the scanner has been recently serviced
    • Whether every image has the same resolution
    • Whether the model segments the correct number of organs
    • Whether predictions assigned a probability match observed frequencies over evaluated cases