Knowledge distillation driven instance segmentation for grading prostate cancer

Hassan, Taimur; Shafay, Muhammad; Hassan, Bilal; Akram, M. Usman; El‐Baz, Ayman; Werghi, Naoufel

doi:10.1016/j.compbiomed.2022.106124

Cited by 10 publications

(7 citation statements)

References 33 publications

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“…Similarly, Kott et al obtained an AUC of 0.82 [ 62 ]. A few articles directly addressed the localization at the pixel level of Gleason patterns [ 28 , 29 , 41 , 67 , 68 , 69 , 70 , 71 ]. Performances vary between IoU of 0.48 [ 70 , 71 ] to IoU around 0.7 [ 28 , 29 , 68 ], Dice score of 0.74 [ 69 ], quadratic Cohen kappa of 0.854 [ 41 ], sensitivity of 0.77 and specificity of 0.94 [ 67 ] (see Table 6 ).…”

Section: Resultsmentioning

confidence: 99%

“…A few articles directly addressed the localization at the pixel level of Gleason patterns [ 28 , 29 , 41 , 67 , 68 , 69 , 70 , 71 ]. Performances vary between IoU of 0.48 [ 70 , 71 ] to IoU around 0.7 [ 28 , 29 , 68 ], Dice score of 0.74 [ 69 ], quadratic Cohen kappa of 0.854 [ 41 ], sensitivity of 0.77 and specificity of 0.94 [ 67 ] (see Table 6 ). Adding epithelium detection greatly improved performance when properly segmenting areas depending on Gleason grades (gain of 0.07 in mean IoU) [ 29 ].…”

Section: Resultsmentioning

confidence: 99%

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Deep Learning Methodologies Applied to Digital Pathology in Prostate Cancer: A Systematic Review

et al. 2023

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Deep learning (DL), often called artificial intelligence (AI), has been increasingly used in Pathology thanks to the use of scanners to digitize slides which allow us to visualize them on monitors and process them with AI algorithms. Many articles have focused on DL applied to prostate cancer (PCa). This systematic review explains the DL applications and their performances for PCa in digital pathology. Article research was performed using PubMed and Embase to collect relevant articles. A Risk of Bias (RoB) was assessed with an adaptation of the QUADAS-2 tool. Out of the 77 included studies, eight focused on pre-processing tasks such as quality assessment or staining normalization. Most articles (n = 53) focused on diagnosis tasks like cancer detection or Gleason grading. Fifteen articles focused on prediction tasks, such as recurrence prediction or genomic correlations. Best performances were reached for cancer detection with an Area Under the Curve (AUC) up to 0.99 with algorithms already available for routine diagnosis. A few biases outlined by the RoB analysis are often found in these articles, such as the lack of external validation. This review was registered on PROSPERO under CRD42023418661.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Deep Learning Methodologies Applied to Digital Pathology in Prostate Cancer: A Systematic Review

et al. 2023

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“… 15 , 31 , 32 , 33 , 49 , 50 , 51 , 52 Additionally, various new techniques have been proposed to improve AI model performance, such as knowledge distillation, deep quantum ordinal regression, and pyramid semantic parsing network. 53 , 54 , 55 …”

Section: Development Of Ai Models For Prostate Cancer Managementmentioning

confidence: 99%

Harnessing artificial intelligence for prostate cancer management

Zhu,

Pan,

Mou

et al. 2024

Cell Reports Medicine

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“…Deep learning exhibits the potential to automate various image analysis tasks, including HIA. This is reinforced by the exceptional performance of deep learning models in histopathology image classification [1], [2], [3], [4], [5], [6], [7] and segmentation [8], [9], [10], [11] tasks. This high performance extends across a range of subjects of interest, including cancer [12], [13], [14], [15], [16], metastases [17], [18], [19], and gene mutation [20], [21], [22].…”

Section: Introductionmentioning

confidence: 99%

Histopathological Cancer Detection Using Intra-Domain Transfer Learning and Ensemble Learning

Yong,

Hum,

Lai

et al. 2024

IEEE Access

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Cancer is a leading global cause of death. Histopathology image analysis is widely recognized as the gold standard for cancer diagnosis, playing a crucial role in early detection and reducing mortality rates. However, this diagnostic task is performed manually by pathologists, leading to high human errors and variabilities due to the huge number of images to screen and tissue complexities. With the emergence of deep learning, specifically the Convolutional Neural Network (CNN), in computer-aided diagnosis, there is a growing interest from the medical community to automate the labor-intensive manual image screening. Despite its promising performance, deep learning models still encounter challenges when it comes to extracting comprehensive histopathological features for optimal results. To tackle this issue, our study introduces deep learning models based on intra-domain transfer learning and ensemble learning. We evaluated these models on public histopathology datasets, including Gastric Histopathology Sub-size Image Database (GasHisSDB), Chaoyang colorectal, and Clinical Proteomic Tumor Analysis Consortium Clear Cell Renal Cell Carcinoma (CPTAC-CCRCC). Our models achieved state-of-the-art accuracy: 99.78% on GasHisSDB, 85.69% on Chaoyang, and 99.17% on CPTAC-CCRCC. These results highlight our models' ability to extract rich features and perform well on low-resolution histopathology images. Thus, our models have the potential to assist pathologists, reduce their workload, and enhance patient survival rates.

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Knowledge distillation driven instance segmentation for grading prostate cancer

Cited by 10 publications

References 33 publications

Deep Learning Methodologies Applied to Digital Pathology in Prostate Cancer: A Systematic Review

Deep Learning Methodologies Applied to Digital Pathology in Prostate Cancer: A Systematic Review

Harnessing artificial intelligence for prostate cancer management

Histopathological Cancer Detection Using Intra-Domain Transfer Learning and Ensemble Learning

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