Essays about: "Biomedical Image Classification"

Showing result 1 - 5 of 6 essays containing the words Biomedical Image Classification.

1. 1. Prediction of the gain in classification performance from combining multiple imaging modalities

   University essay from Uppsala universitet/Institutionen för informationsteknologi

   Author : Roman Denkin; [2023]
   Keywords : ;

   Abstract : In this work, we investigate the relationship between different image modalities and classification performance, aiming to predict the potential gain in classification accuracy when combining multiple modalities. We analyze mathematical and statistical measures and develop novel reconstruction measures (RMSE and RSSIM) to assess information distribution between different image modalities. READ MORE

3. 2. Continual Learning and Biomedical Image Data : Attempting to sequentially learn medical imaging datasets using continual learning approaches

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Davit Soselia; [2022]
   Keywords : Deep Learning; Continual Learning; Catastrophic Forgetting; Biomedical Image Classification; Djup inlärning; kontinuerligt lärande; katastrofal glömska; biomedicinsk bildklassificering;

   Abstract : While deep learning has proved to be useful in a large variety of tasks, a limitation remains of needing all classes and samples to be present at the training stage in supervised problems. This is a major issue in the field of biomedical imaging since keeping samples in the training sets consistently is often a liability. READ MORE

4. 3. Medical image captioning based on Deep Architectures

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Georgios Moschovis; [2022]
   Keywords : Artificial Neural Networks; Deep Learning; Speech and language technology; Natural Language Processing NLP ; Deep networks; Generative deep networks; Convolutional neural networks CNN ; Text generation; Information retrieval; Diagnostic captioning; Image captioning; concept prediction; classification; image encoders; transformers; Encoder-Decoder architecture; abstractive summarization; Neurala nätverk; Djup inlärning; Tal-och språkteknologi; naturlig språkbehandling; djup neurala nätverk; generativa djupa nätverk; konvolutionella neurala nätverk; Textgenerering; Informationssökning; Diagnostisk textning; Bildtextning; konceptförutsägelse; klassificering; bildkodare; transformatorer; kodaravkodararkitektur; abstrakt sammanfattning;

   Abstract : Diagnostic Captioning is described as “the automatic generation of a diagnostic text from a set of medical images of a patient collected during an examination” [59] and it can assist inexperienced doctors and radiologists to reduce clinical errors or help experienced professionals increase their productivity. In this context, tools that would help medical doctors produce higher quality reports in less time could be of high interest for medical imaging departments, as well as significantly impact deep learning research within the biomedical domain, which makes it particularly interesting for people involved in industry and researchers all along. READ MORE

5. 4. Improving Brain Tumor Segmentation using synthetic images from GANs

   University essay from Linköpings universitet/Statistik och maskininlärning

   Author : Aashana Nijhawan; [2021]
   Keywords : Image segmentation; Generative Adversarial Networks; GANs; Computer Vision; synthetic images; generator; discriminator; uncertainty estimation; deep neural networks; U-net; PGGAN;

   Abstract : Artificial intelligence (AI) has been seeing a great amount of hype around it for a few years but more so now in the field of diagnostic medical imaging. AI-based diagnoses have shown improvements in detecting the smallest abnormalities present in tumors and lesions. This can tremendously help public healthcare. READ MORE

6. 5. Convolutional Neural Networks for Classification of Metastatic Tissue in Lymph Nodes : How Does Cutout Affect the Performance of Convolutional Neural Networks for Biomedical Image Classification?

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Andreas Ericsson; Filip Döringer Kana; [2021]
   Keywords : Convolutional neural network; CNN; breast cancer; computer aided diagnostics; data augmentation;

   Abstract : One of every eight women will in their lifetime suffer from breast cancer, making it the most common type of cancer for women. A successful treatment is very much dependent on identifying metastatic tissue which is cancer found beyond the initial tumour. Using deep learning within biomedical analysis has become an effective approach. READ MORE