5 research outputs found
Rotation-Agnostic Image Representation Learning for Digital Pathology
This paper addresses complex challenges in histopathological image analysis
through three key contributions. Firstly, it introduces a fast patch selection
method, FPS, for whole-slide image (WSI) analysis, significantly reducing
computational cost while maintaining accuracy. Secondly, it presents PathDino,
a lightweight histopathology feature extractor with a minimal configuration of
five Transformer blocks and only 9 million parameters, markedly fewer than
alternatives. Thirdly, it introduces a rotation-agnostic representation
learning paradigm using self-supervised learning, effectively mitigating
overfitting. We also show that our compact model outperforms existing
state-of-the-art histopathology-specific vision transformers on 12 diverse
datasets, including both internal datasets spanning four sites (breast, liver,
skin, and colorectal) and seven public datasets (PANDA, CAMELYON16, BRACS,
DigestPath, Kather, PanNuke, and WSSS4LUAD). Notably, even with a training
dataset of 6 million histopathology patches from The Cancer Genome Atlas
(TCGA), our approach demonstrates an average 8.5% improvement in patch-level
majority vote performance. These contributions provide a robust framework for
enhancing image analysis in digital pathology, rigorously validated through
extensive evaluation. Project Page: https://rhazeslab.github.io/PathDino-Page/Comment: 23 pages, 10 figures, 18 tables. Histopathological Image Analysi
Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities
This paper discusses some overlooked challenges faced when working with
machine learning models for histopathology and presents a novel opportunity to
support "Learning Health Systems" with them. Initially, the authors elaborate
on these challenges after separating them according to their mitigation
strategies: those that need innovative approaches, time, or future
technological capabilities and those that require a conceptual reappraisal from
a critical perspective. Then, a novel opportunity to support "Learning Health
Systems" by integrating hidden information extracted by ML models from
digitalized histopathology slides with other healthcare big data is presented
Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review
Numerous machine learning (ML) models have been developed for breast cancer
using various types of data. Successful external validation (EV) of ML models
is important evidence of their generalizability. The aim of this systematic
review was to assess the performance of externally validated ML models based on
histopathology images for diagnosis, classification, prognosis, or treatment
outcome prediction in female breast cancer. A systematic search of MEDLINE,
EMBASE, CINAHL, IEEE, MICCAI, and SPIE conferences was performed for studies
published between January 2010 and February 2022. The Prediction Model Risk of
Bias Assessment Tool (PROBAST) was employed, and the results were narratively
described. Of the 2011 non-duplicated citations, 8 journal articles and 2
conference proceedings met inclusion criteria. Three studies externally
validated ML models for diagnosis, 4 for classification, 2 for prognosis, and 1
for both classification and prognosis. Most studies used Convolutional Neural
Networks and one used logistic regression algorithms. For
diagnostic/classification models, the most common performance metrics reported
in the EV were accuracy and area under the curve, which were greater than 87%
and 90%, respectively, using pathologists' annotations as ground truth. The
hazard ratios in the EV of prognostic ML models were between 1.7 (95% CI,
1.2-2.6) and 1.8 (95% CI, 1.3-2.7) to predict distant disease-free survival;
1.91 (95% CI, 1.11-3.29) for recurrence, and between 0.09 (95% CI, 0.01-0.70)
and 0.65 (95% CI, 0.43-0.98) for overall survival, using clinical data as
ground truth. Despite EV being an important step before the clinical
application of a ML model, it hasn't been performed routinely. The large
variability in the training/validation datasets, methods, performance metrics,
and reported information limited the comparison of the models and the analysis
of their results (...
The Feasibility of the Developing Jurisdiction of the International Criminal Court for Weapons made with Nanotechnology
Nanotechnology and weapons made in the framework of this technology, while having significant positive and negative consequences, criminals abuse it for anti-social and security purposes at the national and international levels. Therefore, this research has been carried out with the aim of expanding the jurisdiction of the International Criminal Court (ICC) and preventing security measures caused by weapons made with nanotechnology. This research aims to identify nanotechnology and its application in the manufacture of weapons and assess and evaluate the jurisdiction of the ICC. The findings of the research show that in addition to controlling the weapons made with nanotechnology and dealing with it with a law and security approach, the ICC can also expand its jurisdiction to prosecute the crime of using weapons made with technology by including crimes related to nanotechnology in the framework and definition of the crime of aggression. Nano should act. Since the crime related to weapons made with nanotechnology is not currently under the judicial jurisdiction of the Court, it seems that according to Article 5 of the Rome Statute, the crime of using weapons made with nanotechnology can be prosecuted as a crime of aggression and the proceedings of the ICC. . Therefore, the ICC, using the existing treaties and regulations, has the possibility to expand its jurisdiction regarding the crime caused by the use of weapons made with nanotechnology and place this crime within the framework of the current regulation
Creating an atlas of normal tissue for pruning WSI patching through anomaly detection
Abstract Patching whole slide images (WSIs) is an important task in computational pathology. While most of them are designed to classify or detect the presence of pathological lesions in a WSI, the confounding role and redundant nature of normal histology are generally overlooked. In this paper, we propose and validate the concept of an “atlas of normal tissue” solely using samples of WSIs obtained from normal biopsies. Such atlases can be employed to eliminate normal fragments of tissue samples and hence increase the representativeness of the remaining patches. We tested our proposed method by establishing a normal atlas using 107 normal skin WSIs and demonstrated how established search engines like Yottixel can be improved. We used 553 WSIs of cutaneous squamous cell carcinoma to demonstrate the advantage. We also validated our method applied to an external dataset of 451 breast WSIs. The number of selected WSI patches was reduced by 30% to 50% after utilizing the proposed normal atlas while maintaining the same indexing and search performance in leave-one-patient-out validation for both datasets. We show that the proposed concept of establishing and using a normal atlas shows promise for unsupervised selection of the most representative patches of the abnormal WSI patches