Towards Confident Detection of Prostate Cancer using High Resolution
Micro-ultrasound
- URL: http://arxiv.org/abs/2207.10485v1
- Date: Thu, 21 Jul 2022 14:00:00 GMT
- Title: Towards Confident Detection of Prostate Cancer using High Resolution
Micro-ultrasound
- Authors: Mahdi Gilany, Paul Wilson, Amoon Jamzad, Fahimeh Fooladgar, Minh
Nguyen Nhat To, Brian Wodlinger, Purang Abolmaesumi, Parvin Mousavi
- Abstract summary: Detection of prostate cancer during transrectal ultrasound-guided biopsy is challenging.
Recent advancements in high-frequency ultrasound imaging - micro-ultrasound - have drastically increased the capability of tissue imaging at high resolution.
Our aim is to investigate the development of a robust deep learning model specifically for micro-ultrasound-guided prostate cancer biopsy.
- Score: 7.826781688190151
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: MOTIVATION: Detection of prostate cancer during transrectal ultrasound-guided
biopsy is challenging. The highly heterogeneous appearance of cancer, presence
of ultrasound artefacts, and noise all contribute to these difficulties. Recent
advancements in high-frequency ultrasound imaging - micro-ultrasound - have
drastically increased the capability of tissue imaging at high resolution. Our
aim is to investigate the development of a robust deep learning model
specifically for micro-ultrasound-guided prostate cancer biopsy. For the model
to be clinically adopted, a key challenge is to design a solution that can
confidently identify the cancer, while learning from coarse histopathology
measurements of biopsy samples that introduce weak labels. METHODS: We use a
dataset of micro-ultrasound images acquired from 194 patients, who underwent
prostate biopsy. We train a deep model using a co-teaching paradigm to handle
noise in labels, together with an evidential deep learning method for
uncertainty estimation. We evaluate the performance of our model using the
clinically relevant metric of accuracy vs. confidence. RESULTS: Our model
achieves a well-calibrated estimation of predictive uncertainty with area under
the curve of 88$\%$. The use of co-teaching and evidential deep learning in
combination yields significantly better uncertainty estimation than either
alone. We also provide a detailed comparison against state-of-the-art in
uncertainty estimation.
Related papers
- Enhancing Trust in Clinically Significant Prostate Cancer Prediction with Multiple Magnetic Resonance Imaging Modalities [61.36288157482697]
In the United States, prostate cancer is the second leading cause of deaths in males with a predicted 35,250 deaths in 2024.
In this paper, we investigate combining multiple MRI modalities to train a deep learning model to enhance trust in the models for clinically significant prostate cancer prediction.
arXiv Detail & Related papers (2024-11-07T12:48:27Z) - Boosting Medical Image-based Cancer Detection via Text-guided Supervision from Reports [68.39938936308023]
We propose a novel text-guided learning method to achieve highly accurate cancer detection results.
Our approach can leverage clinical knowledge by large-scale pre-trained VLM to enhance generalization ability.
arXiv Detail & Related papers (2024-05-23T07:03:38Z) - Improving Breast Cancer Grade Prediction with Multiparametric MRI Created Using Optimized Synthetic Correlated Diffusion Imaging [71.91773485443125]
Grading plays a vital role in breast cancer treatment planning.
The current tumor grading method involves extracting tissue from patients, leading to stress, discomfort, and high medical costs.
This paper examines using optimized CDI$s$ to improve breast cancer grade prediction.
arXiv Detail & Related papers (2024-05-13T15:48:26Z) - ProsDectNet: Bridging the Gap in Prostate Cancer Detection via
Transrectal B-mode Ultrasound Imaging [2.6024562346319167]
ProsDectNet is a multi-task deep learning approach that localizes prostate cancer on B-mode ultrasound.
We trained and validated ProsDectNet using a cohort of 289 patients who underwent MRI-TRUS fusion targeted biopsy.
Our results demonstrate that ProsDectNet has the potential to be used as a computer-aided diagnosis system.
arXiv Detail & Related papers (2023-12-08T19:40:35Z) - Cancer-Net BCa-S: Breast Cancer Grade Prediction using Volumetric Deep
Radiomic Features from Synthetic Correlated Diffusion Imaging [82.74877848011798]
The prevalence of breast cancer continues to grow, affecting about 300,000 females in the United States in 2023.
The gold-standard Scarff-Bloom-Richardson (SBR) grade has been shown to consistently indicate a patient's response to chemotherapy.
In this paper, we study the efficacy of deep learning for breast cancer grading based on synthetic correlated diffusion (CDI$s$) imaging.
arXiv Detail & Related papers (2023-04-12T15:08:34Z) - TRUSformer: Improving Prostate Cancer Detection from Micro-Ultrasound
Using Attention and Self-Supervision [7.503600085603685]
We aim to improve cancer detection by taking a multi-scale, i.e. ROI-scale and biopsy core-scale, approach.
Our model shows consistent and substantial performance improvements compared to ROI-scale-only models.
arXiv Detail & Related papers (2023-03-03T18:12:46Z) - Self-Supervised Learning with Limited Labeled Data for Prostate Cancer
Detection in High Frequency Ultrasound [7.387029659056081]
We apply self-supervised representation learning to micro-ultrasound data to classify cancer from non-cancer tissue.
To the best of our knowledge, this is the first successful end-to-end self-supervised learning approach for prostate cancer detection using ultrasound data.
arXiv Detail & Related papers (2022-11-01T15:28:15Z) - Improving the diagnosis of breast cancer based on biophysical ultrasound
features utilizing machine learning [0.0]
We propose a biophysical feature based machine learning method for breast cancer detection.
The overall accuracy for the most common types and sizes of breast lesions in our study exceeded 98.0% for classification and 0.98 for an area under the receiver operating characteristic curve.
arXiv Detail & Related papers (2022-07-13T23:53:09Z) - Leveraging Uncertainty in Deep Learning for Pancreatic Adenocarcinoma
Grading [0.0]
Pancreatic cancers have one of the worst prognoses compared to other cancers.
Current manual histological grading for diagnosing pancreatic adenocarcinomas is time-consuming and often results in misdiagnosis.
In digital pathology, AI-based cancer grading must be extremely accurate in prediction and uncertainty quantification.
arXiv Detail & Related papers (2022-06-15T19:53:06Z) - Spatio-spectral deep learning methods for in-vivo hyperspectral
laryngeal cancer detection [49.32653090178743]
Early detection of head and neck tumors is crucial for patient survival.
Hyperspectral imaging (HSI) can be used for non-invasive detection of head and neck tumors.
We present multiple deep learning techniques for in-vivo laryngeal cancer detection based on HSI.
arXiv Detail & Related papers (2020-04-21T17:07:18Z) - Stan: Small tumor-aware network for breast ultrasound image segmentation [68.8204255655161]
We propose a novel deep learning architecture called Small Tumor-Aware Network (STAN) to improve the performance of segmenting tumors with different size.
The proposed approach outperformed the state-of-the-art approaches in segmenting small breast tumors.
arXiv Detail & Related papers (2020-02-03T22:25:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.