Vision Transformers for Small Histological Datasets Learned through Knowledge Distillation

Computational Pathology (CPATH) systems have the potential to automate diagnostic tasks. However, the artifacts on the digitized histological glass slides, known as Whole Slide Images (WSIs), may hamper the overall performance of CPATH systems. Deep Learning (DL) models such as Vision Transformers (ViTs) may detect and exclude artifacts before running the diagnostic algorithm. A simple way to develop robust and generalized ViTs is to train them on massive datasets. Unfortunately, acquiring large medical datasets is expensive and inconvenient, prompting the need for a generalized artifact detection method for WSIs. In this paper, we present a student-teacher recipe to improve the classification performance of ViT for the air bubbles detection task. ViT, trained under the student-teacher framework, boosts its performance by distilling existing knowledge from the high-capacity teacher model. Our best-performing ViT yields 0.961 and 0.911 F1-score and MCC, respectively, observing a 7% gain in MCC against stand-alone training. The proposed method presents a new perspective of leveraging knowledge distillation over transfer learning to encourage the use of customized transformers for efficient preprocessing pipelines in the CPATH systems.Comment: Accepted at PAKDD 202

Quantifying the effect of color processing on blood and damaged tissue detection in Whole Slide Images

Histological tissue examination has been a longstanding practice for cancer diagnosis where pathologists identify the presence of tumors on glass slides. Slides acquired from laboratory routine may contain unintentional artifacts due to complications in surgical resection. Blood and damaged tissue artifacts are two common problems associated with transurethral resection of the bladder tumor. Differences in histotechnical procedures among laboratories may also result in color variations and minor inconsistencies in outcome. A digitized version of a glass slide known as a whole slide image (WSI) holds enormous potential for automated diagnostics. The presence of irrelevant areas in a WSI undermines diagnostic value for pathologists as well as computational pathology (CPATH) systems. Therefore, automatic detection and exclusion of diagnostically irrelevant areas may lead to more reliable predictions. In this paper, we are detecting blood and damaged tissue against diagnostically relevant tissue. We gauge the effectiveness of transfer learning against training from scratch. Best models give 0.99 and 0.89 F1 scores for blood and damaged tissue detection. Since blood and damaged tissue have subtle color differences, we assess the impact of color processing methods on the binary classification performance of five well-known architectures. Finally, we remove the color to understand its importance against morphology on classification performance.acceptedVersio

Invasive cancerous area detection in non-muscle invasive bladder cancer whole slide images

Bladder cancer patients’ stratification into risk groups relies on grade, stage and clinical factors. For non-muscle invasive bladder cancer, T1 tumours that invade the subepithelial tissue are high-risk lesions with a high probability to progress into an aggressive muscle-invasive disease. Detecting invasive cancerous areas is the main factor for dictating the treatment strategy for the patient. However, defining invasion is often subject to intra/interobserver variability among pathologists, thus leading to over or undertreatment. Computer-aided diagnosis systems can help pathologists reduce overheads and erratic reproducibility. We propose a multi-scale model that detects invasive cancerous areas patterns across the whole slide image. The model extracts tiles of different tissue types at multiple magnification levels and processes them to predict invasive patterns based on local and regional information for accurate T1 staging. Our proposed method yields an F1 score of 71.9, in controlled settings 74.9, and without infiltration 90.0.acceptedVersio

Quantifying the effect of color processing on blood and damaged tissue detection in Whole Slide Images

Histological tissue examination has been a longstanding practice for cancer diagnosis where pathologists identify the presence of tumors on glass slides. Slides acquired from laboratory routine may contain unintentional artifacts due to complications in surgical resection. Blood and damaged tissue artifacts are two common problems associated with transurethral resection of the bladder tumor. Differences in histotechnical procedures among laboratories may also result in color variations and minor inconsistencies in outcome. A digitized version of a glass slide known as a whole slide image (WSI) holds enormous potential for automated diagnostics. The presence of irrelevant areas in a WSI undermines diagnostic value for pathologists as well as computational pathology (CPATH) systems. Therefore, automatic detection and exclusion of diagnostically irrelevant areas may lead to more reliable predictions. In this paper, we are detecting blood and damaged tissue against diagnostically relevant tissue. We gauge the effectiveness of transfer learning against training from scratch. Best models give 0.99 and 0.89 F1 scores for blood and damaged tissue detection. Since blood and damaged tissue have subtle color differences, we assess the impact of color processing methods on the binary classification performance of five well-known architectures. Finally, we remove the color to understand its importance against morphology on classification performance

Invasive Cancerous Area Detection in Non-Muscle Invasive Bladder Cancer Whole Slide Images

Bladder cancer patients' stratification into risk groups relies on grade, stage and clinical factors. For non-muscle invasive bladder cancer, T1 tumours that invade the subepithelial tissue are high-risk lesions with a high probability to progress into an aggressive muscle-invasive disease. Detecting invasive cancerous areas is the main factor for dictating the treatment strategy for the patient. However, defining invasion is often subject to intra/interobserver variability among pathologists, thus leading to over or undertreatment. Computer-aided diagnosis systems can help pathologists reduce overheads and erratic reproducibility. We propose a multi-scale model that detects invasive cancerous areas patterns across the whole slide image. The model extracts tiles of different tissue types at multiple magnification levels and processes them to predict invasive patterns based on local and regional information for accurate T1 staging. Our proposed method yields an F1 score of 71.9, in controlled settings 74.9, and without infiltration 90.0

Artificial Intelligence in Digital Pathology for Bladder Cancer: Hype or Hope? A Systematic Review

Bladder cancer (BC) diagnosis and prediction of prognosis are hindered by subjective pathological evaluation, which may cause misdiagnosis and under-/over-treatment. Computational pathology (CPATH) can identify clinical outcome predictors, offering an objective approach to improve prognosis. However, a systematic review of CPATH in BC literature is lacking. Therefore, we present a comprehensive overview of studies that used CPATH in BC, analyzing 33 out of 2285 identified studies. Most studies analyzed regions of interest to distinguish normal versus tumor tissue and identify tumor grade/stage and tissue types (e.g., urothelium, stroma, and muscle). The cell’s nuclear area, shape irregularity, and roundness were the most promising markers to predict recurrence and survival based on selected regions of interest, with >80% accuracy. CPATH identified molecular subtypes by detecting features, e.g., papillary structures, hyperchromatic, and pleomorphic nuclei. Combining clinicopathological and image-derived features improved recurrence and survival prediction. However, due to the lack of outcome interpretability and independent test datasets, robustness and clinical applicability could not be ensured. The current literature demonstrates that CPATH holds the potential to improve BC diagnosis and prediction of prognosis. However, more robust, interpretable, accurate models and larger datasets—representative of clinical scenarios—are needed to address artificial intelligence’s reliability, robustness, and black box challenge