Sökning: "faltningsnätverk"

Visar resultat 16 - 20 av 81 uppsatser innehållade ordet faltningsnätverk.

1. 16. Identifying Melanoma Using Transfer Learning and Convolutional Neural Networks : An investigation of skin disease pre-training

   Kandidat-uppsats, KTH/Datavetenskap

   Författare :Albin Wikström Kempe; Elin Inoue; [2022]
   Nyckelord :;

   Sammanfattning : Melanoma, the deadliest form of skin cancer, has become an increasingly common and pressing health issue. Early detection and treatment can be life-saving, but also poses a challenge. A possible solution presents itself in Convolutional Neural Networks (CNNs). LÄS MER

3. 17. Finding Corresponding Regions In Different Mammography Projections Using Convolutional Neural Networks

   Master-uppsats, Linköpings universitet/Institutionen för medicinsk teknik

   Författare :Emil Eriksson; [2022]
   Nyckelord :Machine Learning; Convolutional Neural Network; U-net; Mammography; Corresponding Regions; MLO and CC; Medical Image Processing;

   Sammanfattning : Mammography screenings are performed regularly on women in order to detect early signs of breast cancer, which is the most common form of cancer. During an exam, X-ray images (called mammograms) are taken from two different angles and reviewed by a radiologist. LÄS MER

4. 18. Expressive Automatic Music Transcription : Using hard onset detection to transcribe legato slurs for violin

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Simon Falk; [2022]
   Nyckelord :Automatic Music Transcription; Signal Processing; Convolutional Neural Networks; Onset Detection; Music Information Retrieval; Automatisk musiktranskription; Signalbearbetning; Faltningsnätverk; Ansatsdetektion; Informationssökning av musik;

   Sammanfattning : Automatic Music Transcriptions systems such as ScoreCloud aims to convert audio signals to sheet music. The information contained in sheet music can be divided into increasingly descriptive layers, where most research on Automatic Music Transcription is restricted on note-level transcription and disregard expressive markings such as legato slurs. LÄS MER

5. 19. Deep Learning with Importance Sampling for Brain Tumor MR Segmentation

   Master-uppsats, KTH/Optimeringslära och systemteori

   Författare :Hanna Westermark; [2021]
   Nyckelord :Deep learning; importance sampling; segmentation; convolutional neural networks; MRI; brain tumour; Djupinlärning; importance sampling; segmentering; faltningsnätverk; MRI; hjärntumör;

   Sammanfattning : Segmentation of magnetic resonance images is an important part of planning radiotherapy treat-ments for patients with brain tumours but due to the number of images contained within a scan and the level of detail required, manual segmentation is a time consuming task. Convolutional neural networks have been proposed as tools for automated segmentation and shown promising results. LÄS MER

7. 20. Deep Active Learning for Image Classification using Different Sampling Strategies

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Shahin Saleh; [2021]
   Nyckelord :Convolutional Neural Network; Deep Active Learning; Deep Learning; Image Classification; Sampling Strategies; SemiSupervised Learning.; Bildklassificering; Faltningsnätverk; Deep Active Learning; Djupinlärning; Semiövervakat lärande; Urvalsstrategier.;

   Sammanfattning : Convolutional Neural Networks (CNNs) have been proved to deliver great results in the area of computer vision, however, one fundamental bottleneck with CNNs is the fact that it is heavily dependant on the ground truth, that is, labeled training data. A labeled dataset is a group of samples that have been tagged with one or more labels. LÄS MER