12 citations · 38 across the 8 of their papers we have counts for
12 papers
Decipher-MR: A Vision-Language Foundation Model for 3D MRI Representations
Zhijian Yang, Noel DSouza, Istvan Megyeri +11
Magnetic Resonance Imaging is a critical imaging modality in clinical diagnosis and research, yet its complexity and heterogeneity hinder scalable, generalizable machine learning.…
Multi Anatomy X-Ray Foundation Model
Nishank Singla, Krisztian Koos, Farzin Haddadpour +5
X-ray imaging is a ubiquitous in radiology, yet most existing AI foundation models are limited to chest anatomy and fail to generalize across broader clinical tasks. In this work,…
Multimodal Deep Learning for Subtype Classification in Breast Cancer Using Histopathological Images and Gene Expression Data
Amin Honarmandi Shandiz
Molecular subtyping of breast cancer is crucial for personalized treatment and prognosis. Traditional classification approaches rely on either histopathological images or gene expr…
Automated Identification of Failure Cases in Organ at Risk Segmentation Using Distance Metrics: A Study on CT Data
Amin Honarmandi Shandiz, Attila Rádics, Rajesh Tamada +5
Automated organ at risk (OAR) segmentation is crucial for radiation therapy planning in CT scans, but the generated contours by automated models can be inaccurate, potentially lead…
Adaptation of Tongue Ultrasound-Based Silent Speech Interfaces Using Spatial Transformer Networks
László Tóth, Amin Honarmandi Shandiz, Gábor Gosztolya +1
Thanks to the latest deep learning algorithms, silent speech interfaces (SSI) are now able to synthesize intelligible speech from articulatory movement data under certain condition…
Improved Processing of Ultrasound Tongue Videos by Combining ConvLSTM and 3D Convolutional Networks
Amin Honarmandi Shandiz, Laszlo Toth
Silent Speech Interfaces aim to reconstruct the acoustic signal from a sequence of ultrasound tongue images that records the articulatory movement. The extraction of information ab…
Adaptation of Tacotron2-based Text-To-Speech for Articulatory-to-Acoustic Mapping using Ultrasound Tongue Imaging
Csaba Zainkó, László Tóth, Amin Honarmandi Shandiz +4
For articulatory-to-acoustic mapping, typically only limited parallel training data is available, making it impossible to apply fully end-to-end solutions like Tacotron2. In this p…
Neural Speaker Embeddings for Ultrasound-based Silent Speech Interfaces
Amin Honarmandi Shandiz, László Tóth, Gábor Gosztolya +2
Articulatory-to-acoustic mapping seeks to reconstruct speech from a recording of the articulatory movements, for example, an ultrasound video. Just like speech signals, these recor…
Voice Activity Detection for Ultrasound-based Silent Speech Interfaces using Convolutional Neural Networks
Amin Honarmandi Shandiz, László Tóth
Voice Activity Detection (VAD) is not easy task when the input audio signal is noisy, and it is even more complicated when the input is not even an audio recording. This is the cas…
Improving Neural Silent Speech Interface Models by Adversarial Training
Amin Honarmandi Shandiz, László Tóth, Gábor Gosztolya +2
Besides the well-known classification task, these days neural networks are frequently being applied to generate or transform data, such as images and audio signals. In such tasks,…
Reconstructing Speech from Real-Time Articulatory MRI Using Neural Vocoders
Yide Yu, Amin Honarmandi Shandiz, László Tóth
Several approaches exist for the recording of articulatory movements, such as eletromagnetic and permanent magnetic articulagraphy, ultrasound tongue imaging and surface electromyo…
3D Convolutional Neural Networks for Ultrasound-Based Silent Speech Interfaces
László Tóth, Amin Honarmandi Shandiz
Silent speech interfaces (SSI) aim to reconstruct the speech signal from a recording of the articulatory movement, such as an ultrasound video of the tongue. Currently, deep neural…