Classification Method Based on Deep Learning (DL)Network, for Evaluation of Hormone Status, Can Predict Progression of Breast Cancer

Scientists have developed a classification method based on Deep Learning (DL) network to evaluate hormone status for prognosis of breast cancer. The proposed framework is a reliable alternative to manual methods for automatic grading systems used to determine scoring of estrogen receptor status for predicting progression of breast cancer.

  • Breast cancer is the most common invasive cancer, which accounts for 14% of cancers amongst Indian women, both in rural and urban India.
  • In India, post-cancer survival rate related to breast cancer was reported to be 60% which is approximately 80% of Indian patients younger than 60 years of age.
  • Such alarming numbers could be reduced if the cancer is diagnosed at an early stage.

A team from the Institute of Advanced Study in Science and Technology (IASST), an autonomous institute of the Department of Science & Technology, Govt. of India, have presented the novel Deep Learning (DL) based quantitative evaluation of estrogen or progesterone status with the help of Immunohistochemistry (IHC) specimen to grade for prediction of breast cancer.

  • While breast cancer is generally diagnosed after a visual microscopic examination of a biopsy sample, for prognostic and predictive stratification, IHC molecular marker plays a vital role.
  • IHC strain is used as a prognostic marker in breast cancer pathology and involves a special kind of colour staining for identifying malignant nuclei.
  • It possesses different intensity based on which categories are defined in terms of Allred score (ranges 0 to 3) respectively.
  • Scoring systems called Allred and H-score are used by pathologists in the quantification of the immunohistochemical reaction of estrogen receptor (ER) and progesterone receptor (PR) tissue slides.
  • Hormone receptors, namely estrogen receptor (ER) and progesterone receptor (PR) contribute to predicting cancer progression and associated risk of late recurrence of the disease.

This prompted to find effective solutions towards its management with the assistance of advanced artificial intelligence technologies.

  • The team developed an algorithm that indicated whether or not the cancer cells have hormone receptors on their surface. This study proposed a new method based on deep learning network for precisely segmenting out the stained nuclei region from breast tissue images.
  • The proposed architecture, namely IHC-Net, can semantically segment the exact positive and negative nuclei from tissue images.
  • Finally, an ensemble method is used, which integrates the decision of three machine learning (ML) models for the final Allred cancer score.


New Inventions by DRDO

During the past 3 years, 79 projects amounting to Rs.8201 Crores directly pertaining to development of new defence equipments (i.e.) Cruise Missile, Anti-Ship Missile, Surface-to-Air Missile, Air-to-Air Missile, Extended Range Anti-Submarine Rocket,Mounted Gun System, Ammunitions, Electronic Warfare System, Radars, Torpedoes, High Endurance Autonomous Underwater Vehicle etc. have been undertaken.

Some of the DRDO developed systems which are likely to be available to our defence personnel during 2021-23 are as follow:

Sl. No. System Timelines
1. ASTRA Missile 2021
2. Anti Drone System 2021
3. SATCOM Devices 2021
4. QRSAM 2022
5. ADFCR 2022
6. Helina 2022
7. ADTCR 2022
8. Guided Bomb 2022
9. NAG 2022
10. NGARM 2023
11. SAAW 2023


Many DRDO developed technologies such as Battle Field Surveillance Rader (BFSR), Joint Venture Protective Carbine (JVPC) Jammers, 5.56 mm Rifle, 40 mm Under Barrel Grenade Launcher (UBRL), Oleo Resin (OR) Grenade etc are being utilized by the State Police.

Upgrades to some of the systems have been developed by DRDO.  Details of the same are as follow:-

  • Arjun Mk-1A
  • Akash-NG
  • Light Combat Aircraft Mk-1A
  • Medium Power Radar-Extended Range
  • PINAKA- Extended Range, Guided
  • Electronics & Communication System:  Unified Mission Computer for SU-30 MKI aircraft, Internal EW System for MIG-29 Upgrade Aircraft, EW systems for Naval platforms.

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